Gotowe bibliografie tematyczne / Landmark Estimation

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Artykuły w czasopismach

Gotowa bibliografia na temat „Landmark Estimation”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Landmark Estimation"

Sakai, Atsushi, Teppei Saitoh, and Yoji Kuroda. "Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment." Journal of Robotics and Mechatronics 22, no. 2 (2010): 140–49. http://dx.doi.org/10.20965/jrm.2010.p0140.

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In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.

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Liu, Shengli, Xiaowen Zhu, Zewei Cao, and Gang Wang. "Deep 1D Landmark Representation Learning for Space Target Pose Estimation." Remote Sensing 14, no. 16 (2022): 4035. http://dx.doi.org/10.3390/rs14164035.

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Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.

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Fujii, Hajime, Yoshinobu Ando, Takashi Yoshimi, and Makoto Mizukawa. "Shape Recognition of Metallic Landmark and its Application to Self-Position Estimation for Mobile Robot." Journal of Robotics and Mechatronics 22, no. 6 (2010): 718–25. http://dx.doi.org/10.20965/jrm.2010.p0718.

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This paper proposes a method of improving selfposition estimation accuracy with metallic landmarks for mobile robots. Many methods of the past selfposition estimation researches have used GPS, laserrange scanners, and CCD cameras, but have been unable to obtain landmark information correctly due to environmental factors. Metallic landmarks are useful in environments where conventional sensors do not work well. Self-position estimation accuracy is thus increased by combining metallic landmark information with that from other equipment.

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Floreskul, Volodymyr, Konstantin Tretyakov, and Marlon Dumas. "Memory-Efficient Fast Shortest Path Estimation in Large Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (2014): 91–100. http://dx.doi.org/10.1609/icwsm.v8i1.14532.

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As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmark-based methods have been actively studied in recent years. Landmark-based estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmark-based distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent non-pruned landmark-based methods, while requiring less memory or disk space.

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Fong, Li Wei, Pi Ching Lou, and Ke Jia Tang. "Vehicle Kinematic State Estimation Using Passive Sensor Fusion Approach." Applied Mechanics and Materials 271-272 (December 2012): 1709–12. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1709.

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The main issue addressed here is that of estimating the kinematic state components of a vehicle in autonomous navigation using landmark angle-only measurements from an onboard passive sensor. The estimates of the absolute position and velocity of the vehicle are provided by a hybrid coordinate fusion filter. The hierarchical architecture of the filter which consists of a group of local processors and a global processor is developed for improving estimation accuracy. In each local processor, an extended Kalman filter uses hybrid information from the reference Cartesian coordinate system and the modified polar coordinate system for state and state error covariance extrapolation and updating. In the global processor, a weighted least squares estimator is utilized to combine the outputs of local processors to form a global estimate. By using only two landmarks simulation results show that proposed algorithm improves the estimation accuracy drastically.

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Mohd Shah, Hairol Nizam, Zalina Kamis, Azhar Ahmad, Mohd Rizuan Baharon, Muhd Akmal Noor Rajikon, and Kang Hui Hwa. "Vision Based Position Control for Vertical Take-off and Landing (VTOL) Using One Singular Landmark." Modern Applied Science 13, no. 9 (2019): 33. http://dx.doi.org/10.5539/mas.v13n9p33.

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This project presents a vision based position control for Vertical Take-off and Landing (VTOL) to recognise a singular landmark for landing and take-off. Position control can provide safe flight and an accurate navigation. The circle landmark which used is an artificial landmark at known locations in an environment. Initially, a camera mounted on VTOL facing downward detecting landmarks in environments. A single circle used as landmark and VTOL will be control the position to reach the landmark. The images from the down-looking camera provided vision data to estimates position of VTOL from landmark. A mathematical method based on projective geometry using to locate VTOL on desired landmark from projected point in capture image. By compute the x-y coordinates of the VTOL with respect to landmark, height of camera above landmark will be obtained. VTOL can localize itself in known environment with pose estimation from landmark. The graphic user interface system (GUI) generate by MATLAB software is used to communicate with VTOL to control the VTOL position

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Chen, Yao Chang, Ta Ming Shih, and Chung Ho Wang. "Stereo Vision Specific Observation Model for EKF-Based SLAM." Applied Mechanics and Materials 373-375 (August 2013): 238–41. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.238.

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This work addresses a new probabilistic observation model for a stereo simultaneous localization and mapping (SLAM) system within the standard Extended-Kalman filter (EKF) framework. The observation modal was derived by using the inverse depth parameterization as the landmark modal, and contributes to both bearing and range information into the EKF estimation. In this way the inherently non-linear problem cause by the projection equations is resolved and real depth uncertainty distribution of landmarks features can be accurately estimated. The system was demonstrated with real-world outdoor data. Analysis results show landmark feature depth estimation is more stable and the uncertainty noise converges faster than the traditional approach.

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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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D’Amelio, Richard, and Thomas J. Dunn. "Revisiting the Santa Barbara sense of direction scale, mental rotations, and gender differences in spatial orientation." PsyPag Quarterly 1, no. 115 (2020): 7–10. http://dx.doi.org/10.53841/bpspag.2020.1.115.7.

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Angular direction estimation to landmarks of varying distance in the physical environment was utilised to investigate the ecological validity of the Santa Barbara sense of direction scale (SBSOD). Two- and three-dimensional MR measures were included to enable further the scale applicability. Results showed a moderate correlation between SBSOD and angular deviation from landmarks in the immediate landscape, but not with local or distant landmarks. Moreover, the findings suggest that skills which underlie three-dimensional MR better relate to pointing accuracy (PA) of distant landmarks and the cardinal direction, North. Results also showed a gender-related systematic biases in landmark estimation.

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Chen, Haiwen, Jin Chen, Zhuohuai Guan, Yaoming Li, Kai Cheng, and Zhihong Cui. "Stereovision-Based Ego-Motion Estimation for Combine Harvesters." Sensors 22, no. 17 (2022): 6394. http://dx.doi.org/10.3390/s22176394.

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Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.

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