Journal articles: 'Simultaneuos localization and mapping (SLAM)'

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Saat, Shahrizal, AN MF Airini, Muhammad Salihin Saealal, A. R. Wan Norhisyam, and M. S. Farees Ezwan. "Hector SLAM 2D Mapping for Simultaneous Localization and Mapping (SLAM)." Journal of Engineering and Applied Sciences 14, no. 16 (November 10, 2019): 5610–15. http://dx.doi.org/10.36478/jeasci.2019.5610.5615.

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Tsubouchi, Takashi. "Introduction to Simultaneous Localization and Mapping." Journal of Robotics and Mechatronics 31, no. 3 (June 20, 2019): 367–74. http://dx.doi.org/10.20965/jrm.2019.p0367.

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Simultaneous localization and mapping (SLAM) forms the core of the technology that supports mobile robots. With SLAM, when a robot is moving in an actual environment, real world information is imported to a computer on the robot via a sensor, and robot’s physical location and a map of its surrounding environment of the robot are created. SLAM is a major topic in mobile robot research. Although the information, supported by a mathematical description, is derived from a space in reality, it is formulated based on a probability theory when being handled. Therefore, this concept contributes not only to the research and development concerning mobile robots, but also to the training of mathematics and computer implementation, aimed mainly at position estimation and map creation for the mobile robots. This article focuses on the SLAM technology, including a brief overview of its history, insights from the author, and, finally, introduction of a specific example that the author was involved.

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Boal, Jaime, Álvaro Sánchez-Miralles, and Álvaro Arranz. "Topological simultaneous localization and mapping: a survey." Robotica 32, no. 5 (December 3, 2013): 803–21. http://dx.doi.org/10.1017/s0263574713001070.

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SUMMARYOne of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

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Skrzypczyński, Piotr. "Simultaneous localization and mapping: A feature-based probabilistic approach." International Journal of Applied Mathematics and Computer Science 19, no. 4 (December 1, 2009): 575–88. http://dx.doi.org/10.2478/v10006-009-0045-z.

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Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

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Alsadik, Bashar, and Samer Karam. "The Simultaneous Localization and Mapping (SLAM)-An Overview." Surveying and Geospatial Engineering Journal 2, no. 01 (May 18, 2021): 01–12. http://dx.doi.org/10.38094/sgej1027.

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Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.

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NGUYEN, DuyHinh, Xiqian WU, Daisuke IWAKURA, and Kenzo NONAMI. "2C11 Autonomous control and Simultaneous Localization and Mapping (SLAM) of Unmanned Ground Vehicle." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _2C11–1_—_2C11–9_. http://dx.doi.org/10.1299/jsmemovic.2010._2c11-1_.

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Xu, S., Z. Ji, D. T. Pham, and F. Yu. "Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 3 (September 10, 2010): 733–44. http://dx.doi.org/10.1243/09544062jmes2239.

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This work primarily aims to study robot swarm global mapping in a static indoor environment. Due to the prerequisite estimation of the robots' own poses, it is upgraded to a simultaneous localization and mapping (SLAM) problem. Five techniques are proposed to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule. The EKF mutual localization algorithm updates the pose estimates of not only the current robot, but also the landmark-functioned robots. The arc-carving mapping algorithm is to increase the azimuth resolution of sonar readings by using their freespace regions to shrink the possible regions. It is further improved in both accuracy and efficiency by the creative ideas of bidirectional carving, grid-orientedly correlated-arc carving, working robot group substitution, and termination rule. Software simulation and hardware experiment have verified the feasibility of the proposed SLAM philosophy when implemented in a typical medium-cluttered office by a team of three robots. Besides the combined effect, individual algorithm components have also been investigated.

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Bailey, T., and H. Durrant-Whyte. "Simultaneous localization and mapping (SLAM): part II." IEEE Robotics & Automation Magazine 13, no. 3 (September 2006): 108–17. http://dx.doi.org/10.1109/mra.2006.1678144.

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Saat, Shahrizal, WN Abd Rashid, MZM Tumari, and MS Saealal. "HECTORSLAM 2D MAPPING FOR SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)." Journal of Physics: Conference Series 1529 (April 2020): 042032. http://dx.doi.org/10.1088/1742-6596/1529/4/042032.

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Debeunne, César, and Damien Vivet. "A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping." Sensors 20, no. 7 (April 7, 2020): 2068. http://dx.doi.org/10.3390/s20072068.

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Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

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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 (November 1, 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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Lu, Xiaoyun, Hu Wang, Shuming Tang, Huimin Huang, and Chuang Li. "DM-SLAM: Monocular SLAM in Dynamic Environments." Applied Sciences 10, no. 12 (June 21, 2020): 4252. http://dx.doi.org/10.3390/app10124252.

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Many classic visual monocular SLAM (simultaneous localization and mapping) systems have been developed over the past decades, yet most of them fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM2. This article mainly concentrates on two aspects. Firstly, we proposed a distribution and local-based RANSAC (Random Sample Consensus) algorithm (DLRSAC) to extract static features from the dynamic scene based on awareness of the nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we designed a candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conducted experiments in the public dataset and compared DM-SLAM with ORB-SLAM2. The experiments corroborated the superiority of the DM-SLAM.

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Jilani, Musfira, Padraig Corcoran, and Peter Mooney. "Lampposts as Landmarks for Simultaneous Localization and Mapping." Advanced Materials Research 403-408 (November 2011): 823–29. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.823.

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This paper investigates the effectiveness of using lampposts, which are commonly found in University campus environments with high frequency, as landmarks in a 2D LIDAR based Simultaneous Localization and Mapping (SLAM) framework. Lampposts offer a number of benefits compared to other forms of landmarks. Their unique spatial signature makes it possible to design effective algorithms to extract them. They have a very small spatial size. Their use removes the challenge of determining a corresponding location between difference views. This represents a major challenge if larger objects are used as landmarks. The proposed SLAM algorithm contains three stages. Firstly LIDAR segmentation is performed. Next each object is input to a binary classifier which determines objects with a high probability of corresponding to lampposts. Finally these extracted lampposts are input to an Iterative Closest Point (ICP) based SLAM algorithm. The ICP algorithm used is an extension of the traditional ICP algorithm and filters associations due to noise. Results achieved by the proposed system were very positive. An accurate map of a university’s lampposts was created and localization, when compared to GPS ground-truth, was very accurate.

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Chong, T. J., X. J. Tang, C. H. Leng, M. Yogeswaran, O. E. Ng, and Y. Z. Chong. "Sensor Technologies and Simultaneous Localization and Mapping (SLAM)." Procedia Computer Science 76 (2015): 174–79. http://dx.doi.org/10.1016/j.procs.2015.12.336.

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Wang, Yin Tien, Chen Tung Chi, and Ying Chieh Feng. "Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features." Applied Mechanics and Materials 284-287 (January 2013): 2142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2142.

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An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.

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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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Choset, H., and K. Nagatani. "Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization." IEEE Transactions on Robotics and Automation 17, no. 2 (April 2001): 125–37. http://dx.doi.org/10.1109/70.928558.

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Hou, Guangchao, Qi Shao, Bo Zou, Liwen Dai, Zhe Zhang, Zhehan Mu, Yadong Zhang, and Jingsheng Zhai. "A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network." ISPRS International Journal of Geo-Information 9, no. 1 (December 19, 2019): 5. http://dx.doi.org/10.3390/ijgi9010005.

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The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

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Luo, Kaiqing, Manling Lin, Pengcheng Wang, Siwei Zhou, Dan Yin, and Haolan Zhang. "Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment." Mathematical Problems in Engineering 2020 (September 23, 2020): 1–13. http://dx.doi.org/10.1155/2020/4724310.

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Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.

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Shokrzadeh, P. "SIMULTANEOUS LOCALIZATION AND MAPPING FOR SEMI-SPARSE POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1009–14. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1009-2019.

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Abstract. 3D representation of the environment is a piece of vital information for most of the engineering sciences. However, providing such information in classical surveying approaches demands a considerable amount of time for localizing the sensor in a desired coordinate frame to map the environment. Simultaneous Localization And Mapping (SLAM) algorithm is capable of localizing the sensor and do the mapping while the sensor is moving through the environment. In this paper, SLAM will be applied on the data of a lightweight 3D laser scanner in which we call semi-sparse point cloud, because of the unique specifications of the point cloud which comes from various resolutions in vertical and horizontal directions. In contrast to most of the SLAM algorithms, there is no aiding sensor to provide prior information of motion. The output of the algorithm would be a high-density full geometry detailed map in a short time. The accuracy of the algorithm has been estimated in a medium scale simulated outdoor environments in Gazebo and Robot Operating System (ROS). Considering Velodyne Puck accuracy which is 3 cm, the map was generated with approximately 6 cm accuracy.

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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 (January 26, 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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Nguyen Hoang Thuy, Trang, and Stanislav Shydlouski. "Situations in Construction of 3D Mapping for Slam." MATEC Web of Conferences 155 (2018): 01055. http://dx.doi.org/10.1051/matecconf/201815501055.

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Nowadays, the simultaneous localization and mapping (SLAM) approach has become one of the most advanced engineering methods used for mobile robots to build maps in unknown or inaccessible spaces. Update maps before a certain area while tracking current location and distance. The motivation behind writing this paper is mainly to help us better understand about SLAM and the study situation of SLAM in the world today. Through this, we find the optimal algorithm for moving robots in three dimensions.

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Chen, Chang, Hua Zhu, Menggang Li, and Shaoze You. "A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives." Robotics 7, no. 3 (August 15, 2018): 45. http://dx.doi.org/10.3390/robotics7030045.

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Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

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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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Kimura, Kazushige, James F. Reichert, Omid Ranjbar Pouya, Ahmad Byagowi, Xikui Wang, Debbie M. Kelly, and Zahra Moussavi. "Simultaneous Localization and Mapping (SLAM) for Route Reversal Learning." OBM Geriatrics 2, no. 3 (August 10, 2018): 1. http://dx.doi.org/10.21926/obm.geriatr.1803007.

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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 (November 1, 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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Xiong, Hui, Youping Chen, Xiaoping Li, and Bing Chen. "A two-level optimized graph-based simultaneous localization and mapping algorithm." Industrial Robot: An International Journal 45, no. 6 (October 15, 2018): 758–65. http://dx.doi.org/10.1108/ir-04-2018-0078.

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PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.

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Hsu, Chen-Chien, Wei-Yen Wang, Tung-Yuan Lin, Yin-Tien Wang, and Teng-Wei Huang. "Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots." International Journal of Humanoid Robotics 14, no. 02 (April 16, 2017): 1750007. http://dx.doi.org/10.1142/s0219843617500074.

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FastSLAM, such as FastSLAM 1.0 and FastSLAM 2.0, is a popular algorithm to solve the simultaneous localization and mapping (SLAM) problem for mobile robots. In real environments, however, the execution speed by FastSLAM would be too slow to achieve the objective of real-time design with a satisfactory accuracy because of excessive comparisons of the measurement with all the existing landmarks in particles, particularly when the number of landmarks is drastically increased. In this paper, an enhanced SLAM (ESLAM) is proposed, which uses not only odometer information but also sensor measurements to estimate the robot’s pose in the prediction step. Landmark information that has the maximum likelihood is then used to update the robot’s pose before updating the landmarks’ location. Compared to existing FastSLAM algorithms, the proposed ESLAM algorithm has a better performance in terms of computation efficiency as well as localization and mapping accuracy as demonstrated in the illustrated examples.

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Jiménez, Andrés, Vicente García-Díaz, Rubén González-Crespo, and Sandro Bolaños. "Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems." Sensors 18, no. 8 (August 9, 2018): 2612. http://dx.doi.org/10.3390/s18082612.

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Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system.

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Chen, Xiaohui, Hao Sun, and Heng Zhang. "A New Method of Simultaneous Localization and Mapping for Mobile Robots Using Acoustic Landmarks." Applied Sciences 9, no. 7 (March 30, 2019): 1352. http://dx.doi.org/10.3390/app9071352.

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The simultaneous localization and mapping (SLAM) problem for mobile robots has always been a hotspot in the field of robotics. Simultaneous localization and mapping for robots using visual sensors and laser radar is easily affected by the field of view and ground conditions. According to the problems of traditional sensors applied in SLAM, this paper presents a novel method to perform SLAM using acoustic signals. This method enables robots equipped with sound sources, moving within a working environment and interacting with microphones of interest, to locate itself and map the objects simultaneously. In our case, a method of microphone localization based on a sound source array is proposed, and it was applied as a pre-processing step to the SLAM procedure. A microphone capable of receiving sound signals can be directly used as a feature landmark of a robot observation model without feature extraction. Meanwhile, to eliminate the random error caused by hardware equipment, a sound settled in the middle of two microphones was applied as a calibration sound source to determine the value of the random error. Simulations and realistic experimental results demonstrate the feasibility and effectiveness of the proposed method.

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Pei, Zhaoyi, Songhao Piao, Mohammed Souidi, Muhammad Qadir, and Guo Li. "SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation." Sustainability 10, no. 8 (August 20, 2018): 2946. http://dx.doi.org/10.3390/su10082946.

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The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

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Luca, Razvan, Fritz Tröster, Robert Gall, and Carmen Simion. "Feature Based Mapping Procedure with Application on Simultaneous Localization and Mapping (SLAM)." Solid State Phenomena 166-167 (September 2010): 265–70. http://dx.doi.org/10.4028/www.scientific.net/ssp.166-167.265.

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We are presenting a feature based mapping procedure applied on data reduction to the relevant information used for autonomous navigation. The proceeding is based on the evaluation of the environment using a SICK LD laser scanner. We assume that laser scanners have the advantage of producing reliable data with well understood characteristics for map generation. By implementing evolutive algorithms we process data into lines representing edges of the surrounding objects and create a simplified representation of the environment (feature based). Because of the dynamic generation and evolution of the map, during the movement of the autonomous vehicle we are considering of merging and fitting the data by applying a shape correlation. The goal of our project defines the capability of a fully autonomous vehicle to safely drive through the environment until reaching the standard parking lots and complete autonomous parking procedures.

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Wang, J., and M. Shahbazi. "MAPPING QUALITY EVALUATION OF MONOCULAR SLAM SOLUTIONS FOR MICRO AERIAL VEHICLES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 413–20. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-413-2019.

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Abstract. Monocular simultaneous localization and mapping (SLAM) attracted much attention in the mobile-robotics domain over the past decades along with the advancements of small-format, consumer-grade digital cameras. This is especially the case for micro air vehicles (MAV) due to their payload and power limitations. The quality of global 3D reconstruction by SLAM solutions is a critical factor in occupancy-grid mapping, obstacle avoidance, and map representation. Although several benchmarks have been created in the past to evaluate the quality of vision-based localization and trajectory-estimation, the quality of mapping products has been rarely studied. This paper evaluates the quality of three state-of-the-art open-source monocular SLAM solutions including LSD-SLAM, ORB-SLAM, and LDSO in terms of the geometric accuracy of the global mapping. Since there is no ground-truth information of the testing environment in existing visual SLAM benchmark datasets (e.g., EuRoC, TUM, and KITTI), an evaluation dataset using a quadcopter and a terrestrial laser scanner is created in this work. The dataset is composed of the image data extracted from the recorded videos by flying a drone in the test environment and the high-fidelity point clouds of the test area acquired by a terrestrial laser scanner as the ground truth reference. The mapping quality evaluation of the three SLAM algorithms was mainly conducted on geometric accuracy comparisons by calculating the deviation distance between each SLAM-derived point clouds and the laser-scanned reference. The mapping quality was also discussed with respect to their noise levels as well as further applications.

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Wang, Hongling, Chengjin Zhang, Yong Song, and Bao Pang. "Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments." Journal of Robotics 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/4218324.

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The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.

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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 (September 2, 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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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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Auat Cheein, Fernando A., Fernando M. Lobo Pereira, Fernando di Sciascio, and Ricardo Carelli. "Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation." Knowledge Engineering Review 28, no. 1 (November 2, 2012): 35–57. http://dx.doi.org/10.1017/s0269888912000276.

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AbstractThis paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

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Zhao, Xiong, Tao Zuo, and Xinyu Hu. "OFM-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments." Mathematical Problems in Engineering 2021 (April 8, 2021): 1–16. http://dx.doi.org/10.1155/2021/5538840.

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Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are designed based on the assumption of a static environment, and their robustness and accuracy in the dynamic environment do not behave well. The reason is that moving objects in the scene will cause the mismatch of features in the pose estimation process, which further affects its positioning and mapping accuracy. In the meantime, the three-dimensional semantic map plays a key role in mobile robot navigation, path planning, and other tasks. In this paper, we present OFM-SLAM: Optical Flow combining MASK-RCNN SLAM, a novel visual SLAM for semantic mapping in dynamic indoor environments. Firstly, we use the Mask-RCNN network to detect potential moving objects which can generate masks of dynamic objects. Secondly, an optical flow method is adopted to detect dynamic feature points. Then, we combine the optical flow method and the MASK-RCNN for full dynamic points’ culling, and the SLAM system is able to track without these dynamic points. Finally, the semantic labels obtained from MASK-RCNN are mapped to the point cloud for generating a three-dimensional semantic map that only contains the static parts of the scenes and their semantic information. We evaluate our system in public TUM datasets. The results of our experiments demonstrate that our system is more effective in dynamic scenarios, and the OFM-SLAM can estimate the camera pose more accurately and acquire a more precise localization in the high dynamic environment.

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Yang, Zeng Xiang, and Sai Jin. "UAV Active SLAM Trajectory Programming Based on Optimal Control." Advanced Materials Research 765-767 (September 2013): 1932–35. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1932.

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To decrease the uncertainty of simultaneous localization and mapping of UAV, and at the same time, to increase the speed of searching the unknown environment at which UAV locates, an active SLAM trajectory programming algorithm is proposed based on optimal control. Therefore, UAV SLAM is tackled as a combined optimization problem, considering the precision of UAV location and mapping integrity. Based on the simplified UAV plane motion model, this algorithm is simulated and tested by comparing with the random SLAM algorithm. Simulation results show that this algorithm is effective.

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Yi, Yingmin, and Ying Huang. "Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots." Cybernetics and Information Technologies 14, no. 3 (September 1, 2014): 86–95. http://dx.doi.org/10.2478/cait-2014-0035.

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Abstract The paper proposes landmark sequence data association for Simultaneous Localization and Mapping (SLAM) for data association problem under conditions of noise uncertainty increase. According to the space geometric information of the environment landmarks, the information correlations between the landmarks are constructed based on the graph theory. By observing the variations of the innovation covariance using the landmarks of the adjacent two steps, the problem is converted to solve the landmark TSP problem and the maximum correlation function of the landmark sequences, thus the data association of the observation landmarks is established. Finally, the experiments prove that our approach ensures the consistency of SLAM under conditions of noise uncertainty increase.

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Havangi, Ramazan. "Particle Filter-Based SLAM from Localization Viewpoint." International Journal of Humanoid Robotics 13, no. 03 (August 23, 2016): 1650001. http://dx.doi.org/10.1142/s0219843616500018.

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In order to enhance consistency in simultaneous localization and mapping (SLAM), in this paper, this problem is considered as a solely localization problem in the presence of unknown parameters. In this approach, the proposal distribution is generated based on marginal extended particle filter and static map is considered as a parametric estimation that is estimated by maximum likelihood techniques. Significant improvement of the filtering result from this viewpoint is demonstrated in terms of estimation performance and consistency. Some simulations and experiments are presented to evaluate the algorithm’s performance in comparison to conventional methods.

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LU, ZHEYUAN, ZHENCHENG HU, and KEIICHI UCHIMURA. "SLAM ESTIMATION IN DYNAMIC OUTDOOR ENVIRONMENTS." International Journal of Humanoid Robotics 07, no. 02 (June 2010): 315–30. http://dx.doi.org/10.1142/s021984361000212x.

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This paper describes and compares three different approaches to estimate simultaneous localization and mapping (SLAM) in dynamic outdoor environments. SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, EKF, particle filter). In this paper, a number of promising approaches and recent developments in this literature have been reviewed firstly in this paper. However, SLAM estimation in dynamic outdoor environments has been a difficult task since numerous moving objects exist which may cause bias in feature selection problem. In this paper, we proposed a possibilistic SLAM with RANSAC approach and implemented with three different matching algorithms. Real outdoor experimental result shows the effectiveness and efficiency of our approach.

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Muñoz-Salinas, Rafael, Manuel J. Marín-Jimenez, and R. Medina-Carnicer. "SPM-SLAM: Simultaneous localization and mapping with squared planar markers." Pattern Recognition 86 (February 2019): 156–71. http://dx.doi.org/10.1016/j.patcog.2018.09.003.

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Liu, Jigang, Dongquan Liu, Jun Cheng, and Yuanyan Tang. "Conditional simultaneous localization and mapping: A robust visual SLAM system." Neurocomputing 145 (December 2014): 269–84. http://dx.doi.org/10.1016/j.neucom.2014.05.034.

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Chang, H. Jacky, C. S. George Lee, Yung-Hsiang Lu, and Y. Charlie Hu. "P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction." IEEE Transactions on Robotics 23, no. 2 (April 2007): 281–93. http://dx.doi.org/10.1109/tro.2007.892230.

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Klingensmith, Matthew, Siddartha S. Sirinivasa, and Michael Kaess. "Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)." IEEE Robotics and Automation Letters 1, no. 2 (July 2016): 1156–63. http://dx.doi.org/10.1109/lra.2016.2518242.

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Wang, Zhan, Shoudong Huang, and Gamini Dissanayake. "D-SLAM: A Decoupled Solution to Simultaneous Localization and Mapping." International Journal of Robotics Research 26, no. 2 (February 2007): 187–204. http://dx.doi.org/10.1177/0278364906075173.

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Clemens, Joachim, Tobias Kluth, and Thomas Reineking. "β-SLAM: Simultaneous localization and grid mapping with beta distributions." Information Fusion 52 (December 2019): 62–75. http://dx.doi.org/10.1016/j.inffus.2018.11.005.

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Wu, Yakun, Li Luo, Shujuan Yin, Mengqi Yu, Fei Qiao, Hongzhi Huang, Xuesong Shi, Qi Wei, and Xinjun Liu. "An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment." Applied Sciences 11, no. 4 (February 18, 2021): 1828. http://dx.doi.org/10.3390/app11041828.

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The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.

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Journal articles on the topic 'Simultaneuos localization and mapping (SLAM)'

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