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Zhang, Xuexi, Jiajun Lai, Dongliang Xu, Huaijun Li, and Minyue Fu. "2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots." Journal of Advanced Transportation 2020 (November 16, 2020): 1–14. http://dx.doi.org/10.1155/2020/8867937.
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As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.
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Lahemer and Rad. "An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments." Sensors 19, no. 12 (2019): 2795. http://dx.doi.org/10.3390/s19122795.
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In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.
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Ullah, Inam, Xin Su, Xuewu Zhang, and Dongmin Choi. "Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter." Wireless Communications and Mobile Computing 2020 (June 8, 2020): 1–12. http://dx.doi.org/10.1155/2020/2138643.
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For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.
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Abdul-Rahman, Shuzlina, Mohamad Soffi Abd Razak, Aliya Hasanah Binti Mohd Mushin, Raseeda Hamzah, Nordin Abu Bakar, and Zalilah Abd Aziz. "Simulation of simultaneous localization and mapping using 3D point cloud data." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (2019): 941. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp941-949.
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<span>Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology. Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.</span>
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Zhong, Qiubo, and Xiaoyi Fang. "A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM." Journal of Electrical and Computer Engineering 2021 (July 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/9978022.
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Loop closure detection serves as the fulcrum of improving the accuracy and precision in simultaneous localization and mapping (SLAM). The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. Therefore, a loop closure detection strategy based on an unsupervised learning method is proposed in this paper. The main component adopts BigBiGAN to extract features and establish an original bag of words. Then, the complete bag of words is used to detect loop closing. Finally, a considerable validation check of the ORB descriptor is added to verify the result and output outcome of loop closure detection. The proposed algorithm and other compared algorithms are, respectively, applied on Autolabor Pro1 to execute the indoor visual SLAM. The experiment shows that the proposed algorithm increases the recall rate by 20% compared with ORB-SLAM2 and LSD-SLAM. And it also improves at least 40.0% accuracy than others and reduces 14% time loss of ORB-SLAM2. Therefore, the presented SLAM based on BigBiGAN does benefit much the visual SLAM in the indoor environment.
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Peng, Tao, Dingnan Zhang, Don Lahiru Nirmal Hettiarachchi, and John Loomis. "An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms." Electronic Imaging 2020, no. 6 (2020): 325–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-074.
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Simultaneous Localization and Mapping (SLAM) solves the computational problem of estimating the location of a robot and the map of the environment. SLAM is widely used in the area of navigation, odometry, and mobile robot mapping. However, the performance and efficiency of the small industrial mobile robots and unmanned aerial vehicles (UAVs) are highly constrained to the battery capacity. Therefore, a mobile robot, especially a UAV, requires low power consumption while maintaining high performance. This paper demonstrates holistic and quantitative performance evaluations of embedded computing devices that run on the Nvidia Jetson platform. Evaluations are based on the execution of two state-of-the-art Visual SLAM algorithms, ORB-SLAM2 and OpenVSLAM, on Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson Xavier.
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Titov, R. U., and A. V. Motorin. "Accuracy evaluation of SLAM algorithms." Journal of Physics: Conference Series 1536 (May 2020): 012012. http://dx.doi.org/10.1088/1742-6596/1536/1/012012.
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Nasuriwong, Surasak, and Peerapol Yuwapoositanon. "Gaussian Kernel Posterior Elimination for Fast Look-Ahead Rao-Blackwellised Particle Filtering for SLAM." Applied Mechanics and Materials 781 (August 2015): 555–58. http://dx.doi.org/10.4028/www.scientific.net/amm.781.555.
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In this paper, we explore a method for posterior elimination for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (Fast la-RBPF) algorithm for the simultaneous localization and mapping (SLAM) problem in the probabilistic robotics framework. In the case when a lot of SLAM states need to be estimated, large posterior states associated with the correct state may be outnumbered by multiple non-zero smaller posteriors. We show that by masking the low posterior weight states with a Gaussian kernel prior to weight selection the accuracy of the la-RBPF SLAM algorithm can be improved. Simulation results reveal that integrated with the proposed method the fast la-RBPF SLAM performance is enhanced over both the existing RBPF SLAM and the unmodified la-RBPF SLAM algorithms.
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Kim, Jung-Hee, and Doik Kim. "Computationally Efficient Cooperative Dynamic Range-Only SLAM Based on Sum of Gaussian Filter." Sensors 20, no. 11 (2020): 3306. http://dx.doi.org/10.3390/s20113306.
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A cooperative dynamic range-only simultaneous localization and mapping (CDRO-SLAM) algorithm based on the sum of Gaussian (SoG) filter was recently introduced. The main characteristics of the CDRO-SLAM are (i) the integration of inter-node ranges as well as usual direct robot-node ranges to improve the convergence rate and localization accuracy and (ii) the tracking of any moving nodes under dynamic environments by resetting and updating the SoG variables. In this paper, an efficient implementation of the CDRO-SLAM (eCDRO-SLAM) is proposed to mitigate the high computational burden of the CDRO-SLAM due to the inter-node measurements. Furthermore, a thorough computational analysis is presented, which reveals that the computational efficiency of the eCDRO-SLAM is significantly improved over the CDRO-SLAM. The performance of the proposed eCDRO-SLAM is compared with those of several conventional RO-SLAM algorithms and the results show that the proposed efficient algorithm has a faster convergence rate and a similar map estimation error regardless of the map size. Accordingly, the proposed eCDRO-SLAM can be utilized in various RO-SLAM applications.
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Wang, Hongjian, Guixia Fu, Juan Li, Zheping Yan, and Xinqian Bian. "An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/605981.
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This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.
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Pan, Hai Zhu, and Jin Xue Zhang. "Extending RRT for Robot Motion Planning with SLAM." Applied Mechanics and Materials 151 (January 2012): 493–97. http://dx.doi.org/10.4028/www.scientific.net/amm.151.493.
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In this paper,the motion planning problem for mobile robot is addressed. Motion planning (MP) has diversified over the past few decades to include many different approaches such as cell decomposition, road maps, potential fields, and genetic algorithms. Often the goal of motion planning is not just obstacle avoidance but optimization of certain parameters as well. A motion planning algorithms based on Rapidly-exploring random Tree(RRT) is present in the paper. Then the RRT algorithm has been extended which combines the SLAM algorithm.The Extend-RRT-SLAM has been simulated in MobileSim.Simulation results show Extend-RRT-SLAM to be very effective for robot motion planning.
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Le Large, Nick, Frank Bieder, and Martin Lauer. "Comparison of different SLAM approaches for a driverless race car." tm - Technisches Messen 88, no. 4 (2021): 227–36. http://dx.doi.org/10.1515/teme-2021-0004.
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Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.
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Herranz, F., A. Llamazares, E. Molinos, M. Ocaña, and M. A. Sotelo. "WiFi SLAM algorithms: an experimental comparison." Robotica 34, no. 4 (2014): 837–58. http://dx.doi.org/10.1017/s0263574714001908.
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SUMMARYLocalization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.
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Fіlіmonov, Illia, Anatoly Revko, and Igor Lysenko. "METHODS OF AUTOMATIC DETERMINATION OF THE POSITION OF A MOVING PLATFORM IN SPACE." Technical Sciences and Technologies, no. 2(16) (2019): 71–78. http://dx.doi.org/10.25140/2411-5363-2019-2(16)-71-78.
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Urgency of the research. The existence of the need to improve the accuracy of research areas, including in hard-toreach areas, updates the direction of finding new ways of automatic localization for the task of the robot about collecting the information surrounding it for its own localization and building the environment map. Target setting. Existing methods for determining the location of a moving platform in space have a significant error, which is unacceptable for use in devices designed to operate without human intervention. Actual scientific researches and issues analysis. Research trends show satisfactory results of the introduction of new algorithms based on neural networks, but most of the solutions are designed for large moving platforms, while for small platforms, such as UAVs, solutions are not enough. Uninvestigated parts of general matters defining. Works on the automatic determination of the location of a moving platform often demonstrate the results of experiments that were performed in laboratory conditions. The question arises: how the system will behave when tested in real conditions? The research objective. It is supposed to implement a system of automatic movement of the platform based on modern hardware and localization algorithms. The statement of basic materials. Considered general information for methods of localization and mapping of SLAM, the general algorithm for the operation of methods of SLAM and their mathematical representation are presented. Sensors, that are used in localization tasks, are described and two main SLAM methods with a detailed description are presented. The results of the simulation of the ORB-SLAM2 method were presented. Conclusions. The methods for localizing a moving platform in space and algorithm for the operation of the SLAM methods were reviewed in this paper. The features of SLAM methods and their development prospects are presented.
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Liu, Guohua, Juan Guan, Haiying Liu, and Chenlin Wang. "Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/5819409.
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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.
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Nüchter, A., M. Bleier, J. Schauer, and P. Janotta. "IMPROVING GOOGLE'S CARTOGRAPHER 3D MAPPING BY CONTINUOUS-TIME SLAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (February 23, 2017): 543–49. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w3-543-2017.
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This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google’s results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.
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Ni, Jianjun, Chu Wang, Xinnan Fan, and Simon X. Yang. "A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/905826.
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Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.
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Leng, Xiao Kun, Xin Wei Wang, and Song Hao Piao. "Simultaneous Localization and Mapping for Robot Based Multi-Agent System." Applied Mechanics and Materials 556-562 (May 2014): 2248–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2248.
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Applications on Multi-agent system have been widely studied recently. The positioning of Robotic system is to estimate the position and posture and accurate position estimation. FastSLAM is a SLAM algorithm based on particle filtering, which can perform positioning fast and has been widely applied. This paper applied the genetic particle filtering into SLAM problem to optimize the SLAM algorithm. We present the algorithms based on genetic particle filtering which can obviously reduce the number of particles needed in FastSLAM. The experimental results show that the improvement measures can effectively improve the performance of the algorithm, so that it enables them to maintain a reliable positioning.
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Guoyan, Wang, A. V. Fomichev, and Dy Yiran. "Research on Improved Gaussian Smoothing Filters for SLAM Application." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 12 (2019): 756–64. http://dx.doi.org/10.17587/mau.20.756-764.
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To address the navigation issues of the planetary rover and construct a map for the unknown environment as well as the surface of the planets in our solar system, the simultaneous localization and mapping can be seen as an alternative method. In terms of the navigation with the laser sensor, the Kalman filter and its improving algorithms, such as EKF and UKF are widely used in the the process of processing information. Nevertheless, these filter algorithms suffer from low accuracy and significant computation expensive. The EKF algorithm has a linearization process, the UKF algorithm is better matched in a nonlinear system than the EKF algorithm, but it has more computational complexity. The GP-RTSS filtering algorithm, based on a Gaussian filter, is significantly superior to the EKF and UKF algorithms regarding the sensor fusion accuracy. The Gaussian Process can be used in different non-linear system, does not need prediction model and linearization. However, the main barrier in the process of implementing the GP-RTSS algorithm is that the Gaussian core function requires a lot of computation. In this paper, an algorithm, so-called DIS RTSS filter under a distributed computation scheme, derived from the GP-RTSS Gaussia n smoothing and filter, is proposed. The distributed system can effectively reduce the cost of computation (computation expense and memory). Moreover, four fusion methods for the DIS RTSS filter, i.e., DIS RTP, DIS RTGP, DIS RTB, DIS RTrB are discussed in this paper. The experiments show that among the four algorithms described above, the DIS RTGP algorithm is the most effective solution for practical implementation. The DIS RTSS filtering algorithm can realize a high processing rate and can theoretically process an infinite number of data samples.
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Moratuwage, Diluka, Martin Adams та Felipe Inostroza. "δ-Generalized Labeled Multi-Bernoulli Simultaneous Localization and Mapping with an Optimal Kernel-Based Particle Filtering Approach". Sensors 19, № 10 (2019): 2290. http://dx.doi.org/10.3390/s19102290.
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Under realistic environmental conditions, heuristic-based data association and map management routines often result in divergent map and trajectory estimates in robotic Simultaneous Localization And Mapping (SLAM). To address these issues, SLAM solutions have been proposed based on the Random Finite Set (RFS) framework, which models the map and measurements such that the usual requirements of external data association routines and map management heuristics can be circumvented and realistic sensor detection uncertainty can be taken into account. Rao–Blackwellized particle filter (RBPF)-based RFS SLAM solutions have been demonstrated using the Probability Hypothesis Density (PHD) filter and subsequently the Labeled Multi-Bernoulli (LMB) filter. In multi-target tracking, the LMB filter, which was introduced as an efficient approximation to the computationally expensive δ -Generalized LMB ( δ -GLMB) filter, converts its representation of an LMB distribution to δ -GLMB form during the measurement update step. This not only results in a loss of information yielding inferior results (compared to the δ -GLMB filter) but also fails to take computational advantages in parallelized implementations possible with RBPF-based SLAM algorithms. Similar to state-of-the-art random vector-valued RBPF solutions such as FastSLAM and MH-FastSLAM, the performances of all RBPF-based SLAM algorithms based on the RFS framework also diverge from ground truth over time due to random sampling approaches, which only rely on control noise variance. Further, the methods lose particle diversity and diverge over time as a result of particle degeneracy. To alleviate this problem and further improve the quality of map estimates, a SLAM solution using an optimal kernel-based particle filter combined with an efficient variant of the δ -GLMB filter ( δ -GLMB-SLAM) is presented. The performance of the proposed δ -GLMB-SLAM algorithm, referred to as δ -GLMB-SLAM2.0, was demonstrated using simulated datasets and a section of the publicly available KITTI dataset. The results suggest that even with a limited number of particles, δ -GLMB-SLAM2.0 outperforms state-of-the-art RBPF-based RFS SLAM algorithms.
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Zhang, Yi, and Fei Huang. "Panoramic Visual SLAM Technology for Spherical Images." Sensors 21, no. 3 (2021): 705. http://dx.doi.org/10.3390/s21030705.
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Simultaneous Localization and Mapping (SLAM) technology is one of the best methods for fast 3D reconstruction and mapping. However, the accuracy of SLAM is not always high enough, which is currently the subject of much research interest. Panoramic vision can provide us with a wide range of angles of view, many feature points, and rich information. The panoramic multi-view cross-imaging feature can be used to realize instantaneous omnidirectional spatial information acquisition and improve the positioning accuracy of SLAM. In this study, we investigated panoramic visual SLAM positioning technology, including three core research points: (1) the spherical imaging model; (2) spherical image feature extraction and matching methods, including the Spherical Oriented FAST and Rotated BRIEF (SPHORB) and ternary scale-invariant feature transform (SIFT) algorithms; and (3) the panoramic visual SLAM algorithm. The experimental results show that the method of panoramic visual SLAM can improve the robustness and accuracy of a SLAM system.
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Wang, Yin-Tien, Chen-Tung Chi, and Ying-Chieh Feng. "Robot mapping using local invariant feature detectors." Engineering Computations 31, no. 2 (2014): 297–316. http://dx.doi.org/10.1108/ec-01-2013-0024.
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Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach – SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings – Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value – The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.
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Bayer, Jan, Petr Čížek, and Jan Faigl. "ON CONSTRUCTION OF A RELIABLE GROUND TRUTH FOR EVALUATION OF VISUAL SLAM ALGORITHMS." Acta Polytechnica CTU Proceedings 6 (November 23, 2016): 1–5. http://dx.doi.org/10.14311/app.2016.6.0001.
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In this work we are concerning the problem of localization accuracy evaluation of visual-based Simultaneous Localization and Mapping (SLAM) techniques. Quantitative evaluation of the SLAM algorithm performance is usually done using the established metrics of Relative pose error and Absolute trajectory error which require a precise and reliable ground truth. Such a ground truth is usually hard to obtain, while it requires an expensive external localization system. In this work we are proposing to use the SLAM algorithm itself to construct a reliable ground-truth by offline frame-by-frame processing. The generated ground-truth is suitable for evaluation of different SLAM systems, as well as for tuning the parametrization of the on-line SLAM. The presented practical experimental results indicate the feasibility of the proposed approach.
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Zheng, Bo, Zexu Zhang, Jing Wang, Feng Chen, and Xiangquan Wei. "Body-fixed SLAM with Local Submaps for Planetary Rover." Journal of Navigation 73, no. 1 (2019): 149–71. http://dx.doi.org/10.1017/s0373463319000560.
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In traditional Simultaneous Localisation and Mapping (SLAM) algorithms based on Extended Kalman Filtering (EKF-SLAM), the uncertainty of state estimation will increase rapidly with the development of the exploration process and the increase of map area. Likewise, the computational complexity of the EKF-SLAM is proportional to the square of the number of feature points contained in the state variables in a single filtering process. A new SLAM algorithm combining the local submaps and the body-fixed coordinates of the rover is presented in this paper. The algorithm can reduce the computational complexity and enhance computational speed in consideration of the processing capability of the onboard computer. Due to the introduction of local submaps, the algorithm represented in this paper is able to reduce the number of feature points contained in the state variables in each single filtering process. Therefore, the algorithm could reduce the computational complexity and improve the computational speed. In addition, rover body-fixed SLAM could improve the navigation accuracy of a rover and decrease the cumulative linearization error by coordinates transformation during the update process, which is shown in the simulation results.
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Hsu, Chen-Chien, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang, and Chiang-Heng Chien. "Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots." Engineering Computations 34, no. 4 (2017): 1217–39. http://dx.doi.org/10.1108/ec-05-2015-0123.
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Purpose FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms. Design/methodology/approach As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Findings Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy. Originality/value The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.
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Korkmaz, Mehmet, Nihat Yılmaz, and Akif Durdu. "Comparison of the SLAM algorithms: Hangar experiments." MATEC Web of Conferences 42 (2016): 03009. http://dx.doi.org/10.1051/matecconf/20164203009.
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Kümmerle, Rainer, Bastian Steder, Christian Dornhege, et al. "On measuring the accuracy of SLAM algorithms." Autonomous Robots 27, no. 4 (2009): 387–407. http://dx.doi.org/10.1007/s10514-009-9155-6.
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Rao, Akshay, Wang Han, and P. G. C. N. Senarathne. "A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic." Unmanned Systems 04, no. 04 (2016): 245–54. http://dx.doi.org/10.1142/s2301385016500096.
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Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.
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Dan, Zesheng, Baowang Lian, and Chengkai Tang. "Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity." Remote Sensing 13, no. 9 (2021): 1625. http://dx.doi.org/10.3390/rs13091625.
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In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.
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Wang, Liang, and Zhiqiu Wu. "RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance." Sensors 19, no. 5 (2019): 1050. http://dx.doi.org/10.3390/s19051050.
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Due to image noise, image blur, and inconsistency between depth data and color image, the accuracy and robustness of the pairwise spatial transformation computed by matching extracted features of detected key points in existing sparse Red Green Blue-Depth (RGB-D) Simultaneously Localization And Mapping (SLAM) algorithms are poor. Considering that most indoor environments follow the Manhattan World assumption and the Manhattan Frame can be used as a reference to compute the pairwise spatial transformation, a new RGB-D SLAM algorithm is proposed. It first performs the Manhattan Frame Estimation using the introduced concept of orientation relevance. Then the pairwise spatial transformation between two RGB-D frames is computed with the Manhattan Frame Estimation. Finally, the Manhattan Frame Estimation using orientation relevance is incorporated into the RGB-D SLAM to improve its performance. Experimental results show that the proposed RGB-D SLAM algorithm has definite improvements in accuracy, robustness, and runtime.
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Zhang, Xiaoguo, Qihan Liu, Bingqing Zheng, Huiqing Wang, and Qing Wang. "A visual simultaneous localization and mapping approach based on scene segmentation and incremental optimization." International Journal of Advanced Robotic Systems 17, no. 6 (2020): 172988142097766. http://dx.doi.org/10.1177/1729881420977669.
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Existing visual simultaneous localization and mapping (V-SLAM) algorithms are usually sensitive to the situation with sparse landmarks in the environment and large view transformation of camera motion, and they are liable to generate large pose errors that lead to track failures due to the decrease of the matching rate of feature points. Aiming at the above problems, this article proposes an improved V-SLAM method based on scene segmentation and incremental optimization strategy. In the front end, this article proposes a scene segmentation algorithm considering camera motion direction and angle. By segmenting the trajectory and adding camera motion direction to the tracking thread, an effective prediction model of camera motion in the scene with sparse landmarks and large view transformation is realized. In the back end, this article proposes an incremental optimization method combining segmentation information and an optimization method for tracking prediction model. By incrementally adding the state parameters and reusing the computed results, high-precision results of the camera trajectory and feature points are obtained with satisfactory computing speed. The performance of our algorithm is evaluated by two well-known datasets: TUM RGB-D and NYUDv2 RGB-D. The experimental results demonstrate that our method improves the computational efficiency by 10.2% compared with state-of-the-art V-SLAMs on the desktop platform and by 22.4% on the embedded platform, respectively. Meanwhile, the robustness of our method is better than that of ORB-SLAM2 on the TUM RGB-D dataset.
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Wang, Yin-Tien, and Guan-Yu Lin. "Improvement of speeded-up robust features for robot visual simultaneous localization and mapping." Robotica 32, no. 4 (2013): 533–49. http://dx.doi.org/10.1017/s0263574713000830.
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SUMMARYA robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.
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Du, Shitong, Yifan Li, Xuyou Li, and Menghao Wu. "LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment." Remote Sensing 13, no. 15 (2021): 2864. http://dx.doi.org/10.3390/rs13152864.
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Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.
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Li, Guoqing, Yunhai Geng, and Wenzheng Zhang. "Autonomous planetary rover navigation via active SLAM." Aircraft Engineering and Aerospace Technology 91, no. 1 (2018): 60–68. http://dx.doi.org/10.1108/aeat-12-2016-0239.
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PurposeThis paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.Design/methodology/approachA three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.FindingsThe proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.Originality/valueThis paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.
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Kuo, Bor-Woei, Hsun-Hao Chang, Yung-Chang Chen, and Shi-Yu Huang. "A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map." Journal of Robotics 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/257852.
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Simultaneous Localization and Mapping (SLAM) is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized particle filter- (RBPF-) based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments, that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. Experimental results shows that our work is capable of drawing maps in complex indoor environments, needing only very low amount of memory and much less computational time as compared to other grid map-based RBPF SLAM algorithms.
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Fu, Dong, Hao Xia, and Yanyou Qiao. "Monocular Visual-Inertial Navigation for Dynamic Environment." Remote Sensing 13, no. 9 (2021): 1610. http://dx.doi.org/10.3390/rs13091610.
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Simultaneous localization and mapping (SLAM) systems have been generally limited to static environments. Moving objects considerably reduce the location accuracy of SLAM systems, rendering them unsuitable for several applications. Using a combined vision camera and inertial measurement unit (IMU) to separate moving and static objects in dynamic scenes, we improve the location accuracy and adaptability of SLAM systems in these scenes. We develop a moving object-matched feature points elimination algorithm that uses IMU data to eliminate matches on moving objects but retains them on stationary objects. Moreover, we develop a second algorithm to validate the IMU data to avoid erroneous data from influencing image feature points matching. We test the new algorithms with public datasets and in a real-world experiment. In terms of the root mean square error of the location absolute pose error, the proposed method exhibited higher positioning accuracy for the public datasets than the traditional algorithms. Compared with the closed-loop errors obtained by OKVIS-mono and VINS-mono, those obtained in the practical experiment were lower by 50.17% and 56.91%, respectively. Thus, the proposed method eliminates the matching points on moving objects effectively and achieves feature point matching results that are realistic.
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Li, Jin Liang, and You Xia Sun. "Mapping of Rescue Environment Based on NDT Scan Matching." Advanced Materials Research 760-762 (September 2013): 928–33. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.928.
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This paper studied the mapping problem for rescue robots based on laser scan matching and extend Kalman filtering (EKF). Because of the non-structural rescue environments, it is hard to extract typical features. Scan matching method based on normal distribution transform (NDT) can avoid the hard feature extraction problem by estimation of the probability distribution of laser scan data. By fusing NDT scan matching with EKF framework, the NDT-EKF SLAM algorithm was proposed, which can effectively and precisely build maps for rescue environment. Experiment results show that NDT-EKF SLAM algorithm is more precise than algorithms based solely on scan-matching.
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Kahmen, O., N. Haase, and T. Luhmann. "ORIENTATION OF POINT CLOUDS FOR COMPLEX SURFACES IN MEDICAL SURGERY USING TRINOCULAR VISUAL ODOMETRY AND STEREO ORB-SLAM2." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 35–42. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-35-2020.
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Abstract. In photogrammetry, computer vision and robotics, visual odometry (VO) and SLAM algorithms are well-known methods to estimate camera poses from image sequences. When dealing with unknown scenes there is often no reference data available and also the scene needs to be reconstructed for further analysis. In this contribution a trinocular visual odometry approach is implemented and compared to stereo VO and ORB-SLAM2 in an experimental setup imitating the scene of a knee replacement surgery. Two datasets are analysed. While a test-field provides excellent conditions for feature detection algorithms with its artificial texture assembled, extracted images show the knee joint itself solely in order to use only the homogenous, but in real application stable, region of the knee joint. The camera trajectories of VO and ORB-SLAM2 are transformed to corresponding coordinate systems and are subsequently evaluated. The tracking algorithms show poor quality when only the inappropriate surface of the knee is used but perform well when the artificial texture of the test-field is used. The third camera does not lead to a significant advantage in this setup using our implementation. Possible reasons, e.g. less overlap, are discussed in this contribution. Nevertheless, the quality of the oriented point clouds, obtained by trinocular dense matching, is less than 1mm for most of the analysed data. The experiment will be used to focus on further developments, e.g. dealing with specular reflections, and for evaluation purposes using different SLAM/ VO algorithms.
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Filatov, Anton, and Kirill Krinkin. "A Simplistic Approach for Lightweight Multi-Agent SLAM Algorithm." International Journal of Embedded and Real-Time Communication Systems 11, no. 3 (2020): 67–83. http://dx.doi.org/10.4018/ijertcs.2020070104.
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Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents.
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Tian, Yang, and Shugen Ma. "Kidnapping Detection and Recognition in Previous Unknown Environment." Journal of Sensors 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6468427.
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An unaware event referred to as kidnapping makes the estimation result of localization incorrect. In a previous unknown environment, incorrect localization result causes incorrect mapping result in Simultaneous Localization and Mapping (SLAM) by kidnapping. In this situation, the explored area and unexplored area are divided to make the kidnapping recovery difficult. To provide sufficient information on kidnapping, a framework to judge whether kidnapping has occurred and to identify the type of kidnapping with filter-based SLAM is proposed. The framework is called double kidnapping detection and recognition (DKDR) by performing two checks before and after the “update” process with different metrics in real time. To explain one of the principles of DKDR, we describe a property of filter-based SLAM that corrects the mapping result of the environment using the current observations after the “update” process. Two classical filter-based SLAM algorithms, Extend Kalman Filter (EKF) SLAM and Particle Filter (PF) SLAM, are modified to show that DKDR can be simply and widely applied in existing filter-based SLAM algorithms. Furthermore, a technique to determine the adapted thresholds of metrics in real time without previous data is presented. Both simulated and experimental results demonstrate the validity and accuracy of the proposed method.
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Abouzahir, Mohamed, Abdelhafid Elouardi, Rachid Latif, Samir Bouaziz, and Abdelouahed Tajer. "Embedding SLAM algorithms: Has it come of age?" Robotics and Autonomous Systems 100 (February 2018): 14–26. http://dx.doi.org/10.1016/j.robot.2017.10.019.
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Rodriguez-Losada, Diego, Fernando Matia, Luis Pedraza, Agustin Jimenez, and Ramon Galan. "Consistency of SLAM-EKF Algorithms for Indoor Environments." Journal of Intelligent and Robotic Systems 50, no. 4 (2007): 375–97. http://dx.doi.org/10.1007/s10846-007-9171-8.
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Liu, Jia, Yulei Xie, Shuang Gu, and Xu Chen. "A SLAM-Based Mobile Augmented Reality Tracking Registration Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 01 (2019): 2054005. http://dx.doi.org/10.1142/s0218001420540051.
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This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.
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Liao, Ziwei, Wei Wang, Xianyu Qi, and Xiaoyu Zhang. "RGB-D Object SLAM Using Quadrics for Indoor Environments." Sensors 20, no. 18 (2020): 5150. http://dx.doi.org/10.3390/s20185150.
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Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and planes as landmarks. However, they lack a semantic understanding of the environment. This paper proposes an object-level semantic SLAM algorithm based on RGB-D data, which uses a quadric surface as an object model to compactly represent the object’s position, orientation, and shape. This paper proposes and derives two types of RGB-D camera-quadric observation models: a complete model and a partial model. The complete model combines object detection and point cloud data to estimate a complete ellipsoid in a single RGB-D frame. The partial model is activated when the depth data is severely missing because of illuminations or occlusions, which uses bounding boxes from object detection to constrain objects. Compared with the state-of-the-art quadric SLAM algorithms that use a monocular observation model, the RGB-D observation model reduces the requirements of the observation number and viewing angle changes, which helps improve the accuracy and robustness. This paper introduces a nonparametric pose graph to solve data associations in the back end, and innovatively applies it to the quadric surface model. We thoroughly evaluated the algorithm on two public datasets and an author-collected mobile robot dataset in a home-like environment. We obtained obvious improvements on the localization accuracy and mapping effects compared with two state-of-the-art object SLAM algorithms.
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Толкунова, Ю. М., та Д. О. Подколзіна. "ДОСЛІДЖЕННЯ АЛГОРИТМІВ НАВІГАЦІЇ ТА КАРТОГРАФІЇ ДЛЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТУ". Open Information and Computer Integrated Technologies, № 91 (18 червня 2021): 159–68. http://dx.doi.org/10.32620/oikit.2021.91.12.
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The methods of navigation of an unmanned aerial vehicle (UAV) are considered. The main focus is on navigation methods that reduce the operator's activities to setting the current task and monitoring the operation of the UAV. The UAV must independently assess the environment and plan its path, including in the presence of other moving objects. The solution of control and navigation tasks for UAVs without a remote control significantly facilitates the operator's task, but requires the development of a UAV control system. These tasks include the task of automatically returning the UAV in case of loss of communication with the operator, the solution of which increases the reliability of the navigation system. The change in the nature of the operator's activity, who now does not directly control the movements of the UAV, leads to a change in the nature of the control system. One of the ways to solve this problem is to use an optical navigation system. The analysis of methods and algorithms for optical navigation of an unmanned aerial vehicle: algorithms for local navigation and cartography, correlation-extreme navigation methods and methods of visual odometry. The advantages and disadvantages of optical navigation methods are considered. The use of visual odometry methods has advantages over other methods, but it also has disadvantages associated with the accumulation of errors in the course of the method. Variations of algorithms for simultaneous localization and mapping (SLAM) based on the use of cameras are called visual SLAM. The visual SLAM and visual odometry methods are analyzed. Hybrid SLAM methods solve the problem of error accumulation.The use of UAVs for studying geological processes of the coastline of reservoirs and seas is analyzed. The advantages of using SLAM algorithms for monitoring the state of the coastline are considered. It is concluded that the use of SLAM algorithms for assessing the density of the erosion network of the coastline of reservoirs and seas makes it possible to obtain images without geometric distortions, an optical navigation system based on these algorithms greatly facilitates the operator's task, and will allow the UAV to be returned in case of loss of communication with the operator.
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Khosoussi, Kasra, Matthew Giamou, Gaurav S. Sukhatme, Shoudong Huang, Gamini Dissanayake, and Jonathan P. How. "Reliable Graphs for SLAM." International Journal of Robotics Research 38, no. 2-3 (2019): 260–98. http://dx.doi.org/10.1177/0278364918823086.
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Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.
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Dai, Yan, Liu, Chen, and Huo. "An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud." Remote Sensing 11, no. 20 (2019): 2352. http://dx.doi.org/10.3390/rs11202352.
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3D laser simultaneous localization and mapping (SLAM) technology is one of the most efficient methods to capture spatial information. However, the low-precision of 3D laser SLAM point cloud limits its application in many fields. In order to improve the precision of 3D laser SLAM point cloud, we presented an offline coarse-to-fine precision optimization algorithm. The point clouds are first segmented and registered at the local level. Then, a pose graph of point cloud segments is constructed using feature similarity and global registration. At last, all segments are aligned and merged into the final optimized result. In addition, a cycle based error edge elimination method is utilized to guarantee the consistency of the pose graph. The experimental results demonstrated that our algorithm achieved good performance both in our test datasets and the Cartographer public dataset. Compared with the reference data obtained by terrestrial laser scanning (TLS), the average point-to-point distance root mean square errors (RMSE) of point clouds generated by Google’s Cartographer and LOAM laser SLAM algorithms are reduced by 47.3% and 53.4% respectively after optimization in our datasets. And the average plane-to-plane distances of them are reduced by 50.9% and 52.1% respectively.
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Wang, Jingchuan, and Weidong Chen. "An Improved Extended Information Filter SLAM Algorithm Based on Omnidirectional Vision." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/948505.
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In the SLAM application, omnidirectional vision extracts wide scale information and more features from environments. Traditional algorithms bring enormous computational complexity to omnidirectional vision SLAM. An improved extended information filter SLAM algorithm based on omnidirectional vision is presented in this paper. Based on the analysis of structure a characteristics of the information matrix, this algorithm improves computational efficiency. Considering the characteristics of omnidirectional images, an improved sparsification rule is also proposed. The sparse observation information has been utilized and the strongest global correlation has been maintained. So the accuracy of the estimated result is ensured by using proper sparsification of the information matrix. Then, through the error analysis, the error caused by sparsification can be eliminated by a relocation method. The results of experiments show that this method makes full use of the characteristic of repeated observations for landmarks in omnidirectional vision and maintains great efficiency and high reliability in mapping and localization.
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Wang, Tianmiao, Chaolei Wang, Jianhong Liang, and Yicheng Zhang. "Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition." Industrial Robot: An International Journal 41, no. 3 (2014): 266–74. http://dx.doi.org/10.1108/ir-07-2013-378.
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Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.
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Zhang, Chenyang, Teng Huang, Rongchun Zhang, and Xuefeng Yi. "PLD-SLAM: A New RGB-D SLAM Method with Point and Line Features for Indoor Dynamic Scene." ISPRS International Journal of Geo-Information 10, no. 3 (2021): 163. http://dx.doi.org/10.3390/ijgi10030163.
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RGB-D SLAM (Simultaneous Localization and Mapping) generally performs smoothly in a static environment. However, in dynamic scenes, dynamic features often cause wrong data associations, which degrade accuracy and robustness. To address this problem, in this paper, a new RGB-D dynamic SLAM method, PLD-SLAM, which is based on point and line features for dynamic scenes, is proposed. First, to avoid under-over segmentation caused by deep learning, PLD-SLAM combines deep learning for semantic information segmentation with the K-Means clustering algorithm considering depth information to detect the underlying dynamic features. Next, two consistency check strategies are utilized to check and filter out the dynamic features more reasonably. Then, to obtain a better practical performance, point and line features are utilized to calculate camera pose in the dynamic SLAM, which is also different from most published dynamic SLAM algorithms based merely on point features. The optimization model with point and line features is constructed and utilized to calculate the camera pose with higher accuracy. Third, enough experiments on the public TUM RGB-D dataset and the real-world scenes are conducted to verify the location accuracy and performance of PLD-SLAM. We compare our experimental results with several state-of-the-art dynamic SLAM methods in terms of average localization errors and the visual difference between the estimation trajectories and the ground-truth trajectories. Through the comprehensive comparisons with these dynamic SLAM schemes, it can be fully demonstrated that PLD-SLAM can achieve comparable or better performances in dynamic scenes. Moreover, the feasibility of camera pose estimation based on both point features and line features has been proven by the corresponding experiments from a comparison with our proposed PLD-SLAM only based on point features.