Journal articles on the topic 'Graph-based localization and mapping'
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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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Xu, Hao, Huafei Sun, Yongqiang Cheng, and Hao Liu. "Wireless sensor networks localization based on graph embedding with polynomial mapping." Computer Networks 106 (September 2016): 151–60. http://dx.doi.org/10.1016/j.comnet.2016.06.032.
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Zhu, Zihan, Yi Zhang, Weijun Wang, Wei Feng, Haowen Luo, and Yaojie Zhang. "Adaptive Adjustment of Factor’s Weight for a Multi-Sensor SLAM." Journal of Physics: Conference Series 2451, no. 1 (March 1, 2023): 012004. http://dx.doi.org/10.1088/1742-6596/2451/1/012004.
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Abstract A multi-sensor fusion simultaneous localization and mapping(SLAM) method based on factor graph optimization that can adaptively modify the weight of the graph factor is proposed in this study, to enhance the localization and mapping capability of autonomous robots in tough situations. Firstly, the algorithm fuses multi-lines lidar, monocular camera, and inertial measurement unit(IMU) in the odometry. Second, the factor graph is constructed using lidar and visual odometry as the unary edge and binary edge constraints, respectively, with the motion determined by IMU odometry serving as the primary odometry in the system. Finally, different increments of IMU odometry, lidar odometry and visual odometry are computed as favor factors to realize the adaptive adjustment of the factor’s weight. The suggested method has greater location accuracy and a better mapping effect in complex situations when compared to previous algorithms.
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Mukherjee, Shohin, Michael Kaess, Joseph N. Martel, and Cameron N. Riviere. "EyeSAM: graph-based localization and mapping of retinal vasculature during intraocular microsurgery." International Journal of Computer Assisted Radiology and Surgery 14, no. 5 (February 21, 2019): 819–28. http://dx.doi.org/10.1007/s11548-019-01925-1.
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Ren, Zhuli, Liguan Wang, and Lin Bi. "Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment." Sensors 19, no. 13 (July 1, 2019): 2915. http://dx.doi.org/10.3390/s19132915.
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Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.
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Dai, Kai, Bohua Sun, Guanpu Wu, Shuai Zhao, Fangwu Ma, Yufei Zhang, and Jian Wu. "LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios." Journal of Imaging 9, no. 2 (February 20, 2023): 52. http://dx.doi.org/10.3390/jimaging9020052.
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LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.
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Zhang Tianxi, 张天喜, 周军 Zhou Jun, 廖华丽 Liao Huali, and 杨跟 Yang Gen. "Simultaneous Localization and Mapping Strategy of Graph Optimization Based on Three-Dimensional Laser." Laser & Optoelectronics Progress 56, no. 20 (2019): 201502. http://dx.doi.org/10.3788/lop56.201502.
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Wu, Xinzhao, Peiqing Li, Qipeng Li, and Zhuoran Li. "Two-dimensional-simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation." Sustainability 15, no. 2 (January 8, 2023): 1172. http://dx.doi.org/10.3390/su15021172.
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A robust multi-sensor fusion simultaneous localization and mapping (SLAM) algorithm for complex road surfaces is proposed to improve recognition accuracy and reduce system memory occupation, aiming to enhance the computational efficiency of light detection and ranging in complex environments. First, a weighted signed distance function (W-SDF) map-based SLAM method is proposed. It uses a W-SDF map to capture the environment with less accuracy than the raster size but with high localization accuracy. The Levenberg–Marquardt method is used to solve the scan-matching problem in laser SLAM; it effectively alleviates the limitations of the Gaussian–Newton method that may lead to insufficient local accuracy, and reduces localisation errors. Second, ground constraint factors are added to the factor graph, and a multi-sensor fusion localisation algorithm is proposed based on factor graph elimination optimisation. A sliding window is added to the chain factor graph model to retain the historical state information within the window and avoid high-dimensional matrix operations. An elimination algorithm is introduced to transform the factor graph into a Bayesian network to marginalize the historical states and reduce the matrix dimensionality, thereby improving the algorithm localisation accuracy and reducing the memory occupation. Finally, the proposed algorithm is compared and validated with two traditional algorithms based on an unmanned cart. Experiments show that the proposed algorithm reduces memory consumption and improves localisation accuracy compared to the Hector algorithm and Cartographer algorithm, has good performance in terms of accuracy, reliability and computational efficiency in complex pavement environments, and is better utilised in practical environments.
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Xu, Shaoyan, Tao Wang, Congyan Lang, Songhe Feng, and Yi Jin. "Graph-based visual odometry for VSLAM." Industrial Robot: An International Journal 45, no. 5 (August 20, 2018): 679–87. http://dx.doi.org/10.1108/ir-04-2018-0061.
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Purpose Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information. Design/methodology/approach Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner. Findings The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general. Originality/value Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.
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OKADA, Nobuya, Daichi ABE, Satoshi SUZUKI, Kojiro IIZUKA, and Takashi KAWAMURA. "2A2-R04 Image and Shape features combined Landmarks based Graph SLAM(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _2A2—R04_1—_2A2—R04_4. http://dx.doi.org/10.1299/jsmermd.2014._2a2-r04_1.
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Dine, Abdelhamid, Abdelhafid Elouardi, Bastien Vincke, and Samir Bouaziz. "Graph-Based Simultaneous Localization and Mapping: Computational Complexity Reduction on a Multicore Heterogeneous Architecture." IEEE Robotics & Automation Magazine 23, no. 4 (December 2016): 160–73. http://dx.doi.org/10.1109/mra.2016.2580466.
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Wang, Zhan, Alain Lambert, and Xun Zhang. "Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments." Computers 8, no. 3 (September 2, 2019): 63. http://dx.doi.org/10.3390/computers8030063.
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Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.
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Zhao, Junqiao, Yewei Huang, Xudong He, Shaoming Zhang, Chen Ye, Tiantian Feng, and Lu Xiong. "Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking." Sensors 19, no. 1 (January 4, 2019): 161. http://dx.doi.org/10.3390/s19010161.
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Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.
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Liu, Yonghui, Weimin Zhang, Fangxing Li, Zhengqing Zuo, and Qiang Huang. "Real-Time Lidar Odometry and Mapping with Loop Closure." Sensors 22, no. 12 (June 9, 2022): 4373. http://dx.doi.org/10.3390/s22124373.
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Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency.
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Ktiri, Youssef, Tomoaki YOSHIKAI, and Masayuki INABA. "2A1-M15 Enhancing Localization Using Random Ferns Based Vision and Multi-Robot Collaboration(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2A1—M15_1—_2A1—M15_4. http://dx.doi.org/10.1299/jsmermd.2011._2a1-m15_1.
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Li, M., and F. Rottensteiner. "VISION-BASED INDOOR LOCALIZATION VIA A VISUAL SLAM APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 827–33. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-827-2019.
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<p><strong>Abstract.</strong> With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image.</p>
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Das, Anweshan, Jos Elfring, and Gijs Dubbelman. "Real-Time Vehicle Positioning and Mapping Using Graph Optimization." Sensors 21, no. 8 (April 16, 2021): 2815. http://dx.doi.org/10.3390/s21082815.
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In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture’s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error’s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.
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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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Xia, Linlin, Ruimin Liu, Daochang Zhang, and Jingjing Zhang. "Polarized light-aided visual-inertial navigation system: global heading measurements and graph optimization-based multi-sensor fusion." Measurement Science and Technology 33, no. 5 (February 17, 2022): 055111. http://dx.doi.org/10.1088/1361-6501/ac4637.
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Abstract Polarized skylight is as fundamental a constituent of passive navigation as the geomagnetic field. With regard to its applicability to outdoor robot localization, a polarized light-aided visual-inertial navigation system (VINS) modelization dedicated to globally optimized pose estimation and heading correction is constructed. The combined system follows typical visual simultaneous localization and mapping (SLAM) frameworks, and we propose a methodology to fuse global heading measurements with visual and inertial information in a graph optimization-based estimator. With ideas of‘adding new attributes of graph vertices and creating heading error-encoded constraint edges’, the heading, as the absolute orientation reference, is estimated by the Berry polarization model and continuously updated in a graph structure. The formulized graph optimization process for multi-sensor fusion is simultaneously provided. In terms of campus road experiments on the Bulldog-CX robot platform, the results are compared against purely stereo camera-dependent and VINS Fusion frameworks, revealing that our design is substantially more accurate than others with both locally and globally consistent position and attitude estimates. As a passive and tightly coupled navigation mode, the polarized light-aided VINS can therefore be considered as a tool candidate for a class of visual SLAM-based multi-sensor fusion.
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Yamanaka, Satoshi, and Kazuyuki Morioka. "2A1-O09 Development of SLAM algorithm with hybrid mapping based on occupancy grid map and graph structure(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2A1—O09_1—_2A1—O09_4. http://dx.doi.org/10.1299/jsmermd.2011._2a1-o09_1.
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Zhang, Chuanwei, Lei Lei, Xiaowen Ma, Rui Zhou, Zhenghe Shi, and Zhongyu Guo. "Map Construction Based on LiDAR Vision Inertial Multi-Sensor Fusion." World Electric Vehicle Journal 12, no. 4 (December 12, 2021): 261. http://dx.doi.org/10.3390/wevj12040261.
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In order to make up for the shortcomings of independent sensors and provide more reliable estimation, a multi-sensor fusion framework for simultaneous localization and mapping is proposed in this paper. Firstly, the light detection and ranging (LiDAR) point cloud is screened in the front-end processing to eliminate abnormal points and improve the positioning and mapping accuracy. Secondly, for the problem of false detection when the LiDAR is surrounded by repeated structures, the intensity value of the laser point cloud is used as the screening condition to screen out robust visual features with high distance confidence, for the purpose of softening. Then, the initial factor, registration factor, inertial measurement units (IMU) factor and loop factor are inserted into the factor graph. A factor graph optimization algorithm based on a Bayesian tree is used for incremental optimization estimation to realize the data fusion. The algorithm was tested in campus and real road environments. The experimental results show that the proposed algorithm can realize state estimation and map construction with high accuracy and strong robustness.
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Ravankar, Ankit, Abhijeet Ravankar, Yukinori Kobayashi, Lv Jixin, and Takanori Emaru. "2A2-M06 Vision based Localization and Mapping for Indoor Robots using RGBD Sensor." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _2A2—M06_1—_2A2—M06_3. http://dx.doi.org/10.1299/jsmermd.2015._2a2-m06_1.
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Zhou, Zou, Guoli Zhang, Fei Zheng, Tuyang Wang, Longjie Chen, and Nan Duan. "A Graph Optimization-Based Acoustic SLAM Edge Computing System Offering Centimeter-Level Mapping Services with Reflector Recognition Capability." Security and Communication Networks 2021 (December 3, 2021): 1–17. http://dx.doi.org/10.1155/2021/9126833.
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Robots can use echo signals for simultaneous localization and mapping (SLAM) services in unknown environments where its own camera is not available. In current acoustic SLAM solutions, the time of arrival (TOA) in the room impulse response (RIR) needs to be associated with the corresponding reflected wall, which leads to an echo labelling problem (ELP). The position of the wall can be derived from the TOA associated with the wall, but most of the current solutions ignore the effect of the cumulative error in the robot’s moving state measurement on the wall position estimation. In addition, the estimated room map contains only the shape information of the room and lacks position information such as the positions of doors and windows. To address the above problems, this paper proposes a graph optimization-based acoustic SLAM edge computing system offering centimeter-level mapping services with reflector recognition capability. In this paper, a robot equipped with a sound source and a four-channel microphone array travels around the room, and it can collect the room impulse response at different positions of the room and extract the RIR cepstrum feature from the room impulse response. The ELP is solved by using the RIR cepstrum to identify reflectors with different absorption coefficients. Then, the similarity of the RIR cepstrum vectors is used for closed-loop detection. Finally, this paper proposes a method to eliminate the cumulative error of robot movement by fusing IMU data and acoustic echo data using graph-optimized edge computation. The experiments show that the acoustic SLAM system in this paper can accurately estimate the trajectory of the robot and the position of doors, windows, and so on in the room map. The average self-localization error of the robot is 2.84 cm, and the mapping error is 4.86 cm, which meet the requirement of centimeter-level map service.
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Wen, Shuhuan, Jian Chen, Xiaohan Lv, and Yongzheng Tong. "Cooperative simultaneous localization and mapping algorithm based on distributed particle filter." International Journal of Advanced Robotic Systems 16, no. 1 (January 1, 2019): 172988141881995. http://dx.doi.org/10.1177/1729881418819950.
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In this article, cooperative simultaneous localization and mapping algorithm based on distributed particle filter is proposed for multi-robot cooperative simultaneous localization and mapping system. First, a multi-robot cooperative simultaneous localization and mapping system model is established based on Rao-Blackwellised particle filter and simultaneous localization and mapping (FastSLAM 2.0) algorithm, and an median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm combined with the M-posterior distributed estimation algorithm is proposed. Then, according to the accuracy advantage of the early landmarks comparing to the later landmarks in the simultaneous localization and mapping task, an improved time-median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm based on time difference optimization is proposed, which optimizes the weights of the local estimation and improves the accuracy of the global estimation. The simulation results show that the algorithm is practical and effective.
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Osipov, Alexander, Mikhail Ostanin, and Alexandr Klimchik. "Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization." Information 14, no. 3 (February 24, 2023): 149. http://dx.doi.org/10.3390/info14030149.
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State-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This approach is independent of the type of feature descriptors and SLAM used onboard the AR/MR device. The mesh could be reduced to the point cloud that only takes vertices. We analyzed and compared different point cloud registration methods applicable to the problem. In addition, we proposed a new pipeline Feature Inliers Graph Registration Approach (FIGRA) for the co-localization of AR/MR devices using point clouds. The comparative analysis of Go-ICP, Bayesian-ICP, FGR, Teaser++, and FIGRA shows that feature-based methods are more robust and faster than ICP-based methods. Through an in-depth comparison of the feature-based methods with the usual fast point feature histogram and the new weighted height image descriptor, we found that FIGRA has a better performance due to its effective graph-theoretic base. The proposed pipeline allows one to match point clouds in complex real scenarios with low overlap and sparse point density.
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Ren, Ruike, Hao Fu, and Meiping Wu. "Large-Scale Outdoor SLAM Based on 2D Lidar." Electronics 8, no. 6 (May 31, 2019): 613. http://dx.doi.org/10.3390/electronics8060613.
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For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.
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Bonin-Font, Francisco, and Antoni Burguera. "Towards Multi-Robot Visual Graph-SLAM for Autonomous Marine Vehicles." Journal of Marine Science and Engineering 8, no. 6 (June 14, 2020): 437. http://dx.doi.org/10.3390/jmse8060437.
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State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, in a common reference system, several trajectories of different robots that participate simultaneously in a common mission. One of the main problems in centralized configurations, where the leader can receive multiple data from the rest of robots, is the limited communications bandwidth that delays the data transmission and can be overloaded quickly, restricting the reactive actions. This paper presents a new approach to Multi-robot visual graph Simultaneous Localization and Mapping (SLAM) that aims to perform a joined topological map, which evolves in different directions according to the different trajectories of the different robots. The main contributions of this new strategy are centered on: (a) reducing to hashes of small dimensions the visual data to be exchanged among all agents, diminishing, in consequence, the data delivery time, (b) running two different phases of SLAM, intra- and inter-session, with their respective loop-closing tasks, with a trajectory joining action in between, with high flexibility in their combination, (c) simplifying the complete SLAM process, in concept and implementation, and addressing it to correct the trajectory of several robots, initially and continuously estimated by means of a visual odometer, and (d) executing the process online, in order to assure a successful accomplishment of the mission, with the planned trajectories and at the planned points. Primary results included in this paper show a promising performance of the algorithm in visual datasets obtained in different points on the coast of the Balearic Islands, either by divers or by an Autonomous Underwater Vehicle (AUV) equipped with cameras.
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Alsadik, Bashar, and Samer Karam. "The Simultaneous Localization and Mapping (SLAM)-An Overview." Journal of Applied Science and Technology Trends 2, no. 04 (November 18, 2021): 120–31. http://dx.doi.org/10.38094/jastt204117.
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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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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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Palomeras, Narcís, Marc Carreras, and Juan Andrade-Cetto. "Active SLAM for Autonomous Underwater Exploration." Remote Sensing 11, no. 23 (November 28, 2019): 2827. http://dx.doi.org/10.3390/rs11232827.
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Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.
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Du, Shi Biao, Xin Zhao, and Hong Yong Fu. "3D Mapping and Positioning Technology of Lunar Environment Based on LiDAR." Journal of Physics: Conference Series 2364, no. 1 (November 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2364/1/012010.
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Abstract -Using intelligent guidance system for lunar sample collection, Simultaneous lunar environment 3D mapping and localization technology (SLAM) is a key problem to be solved urgently. The lunar illumination changes dramatically and the terrain is complex. And limited computing power of the intelligent guidance system on the moon will affect the positioning accuracy of the algorithm. In view of the low positioning accuracy of LiDAR in the current lunar environment. This paper use an algorithm with higher positioning accuracy, which integrates the front-end feature extraction algorithm, the back-end optimization algorithm based on factor graph and the Scan Context loop closure detection algorithm. The simulation platform of lunar environment is built by Airsim to test the algorithm, and the robustness and accuracy of the algorithm are verified.
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Gao, Peng, and Hao Zhang. "Long-Term Loop Closure Detection through Visual-Spatial Information Preserving Multi-Order Graph Matching." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10369–76. http://dx.doi.org/10.1609/aaai.v34i06.6604.
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Loop closure detection is a fundamental problem for simultaneous localization and mapping (SLAM) in robotics. Most of the previous methods only consider one type of information, based on either visual appearances or spatial relationships of landmarks. In this paper, we introduce a novel visual-spatial information preserving multi-order graph matching approach for long-term loop closure detection. Our approach constructs a graph representation of a place from an input image to integrate visual-spatial information, including visual appearances of the landmarks and the background environment, as well as the second and third-order spatial relationships between two and three landmarks, respectively. Furthermore, we introduce a new formulation that formulates loop closure detection as a multi-order graph matching problem to compute a similarity score directly from the graph representations of the query and template images, instead of performing conventional vector-based image matching. We evaluate the proposed multi-order graph matching approach based on two public long-term loop closure detection benchmark datasets, including the St. Lucia and CMU-VL datasets. Experimental results have shown that our approach is effective for long-term loop closure detection and it outperforms the previous state-of-the-art methods.
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Jung, Sungwook, Duckyu Choi, Seungwon Song, and Hyun Myung. "Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM." Remote Sensing 12, no. 18 (September 16, 2020): 3022. http://dx.doi.org/10.3390/rs12183022.
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With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge inspection using an aerial vehicle, as well as a technical method for UAV control for actually conducting bridge inspections. Due to the harsh environment involved and the corresponding limitations on GPS usage, a graph-based SLAM approach using a tilted 3D LiDAR (Light Detection and Ranging) and a monocular camera to localize the UAV and map the target bridge is proposed. Each visual-inertial state estimate and the corresponding LiDAR sweep are combined into a single subnode. These subnodes make up a “supernode” that consists of state estimations and accumulated scan data for robust and stable node generation in graph SLAM. The constraints are generated from LiDAR data using the normal distribution transform (NDT) and generalized iterative closest point (G-ICP) matching. The feasibility of the proposed method was verified on two different types of bridges: on the ground and offshore.
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Wang, Fei, Xiaogang Ruan, Pengfei Dong, and OUATTARA SIE. "A Micro SLAM System Based on ORB for RGB-D Cameras." MATEC Web of Conferences 160 (2018): 07001. http://dx.doi.org/10.1051/matecconf/201816007001.
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In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments.
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Dubé, Renaud, Andrei Cramariuc, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, and Cesar Cadena. "SegMap: Segment-based mapping and localization using data-driven descriptors." International Journal of Robotics Research 39, no. 2-3 (July 10, 2019): 339–55. http://dx.doi.org/10.1177/0278364919863090.
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Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.
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Chen, Chao, Yukai Ma, Jiajun Lv, Xiangrui Zhao, Laijian Li, Yong Liu, and Wang Gao. "OL-SLAM: A Robust and Versatile System of Object Localization and SLAM." Sensors 23, no. 2 (January 10, 2023): 801. http://dx.doi.org/10.3390/s23020801.
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This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios.
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Zhao, Junqiao, Xudong He, Jun Li, Tiantian Feng, Chen Ye, and Lu Xiong. "Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR." Remote Sensing 11, no. 14 (July 21, 2019): 1726. http://dx.doi.org/10.3390/rs11141726.
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The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.
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Sujan, Vivek Anand, Marco Antonio Meggiolaro, and Felipe Augusto Weilemann Belo. "A new technique in mobile robot simultaneous localization and mapping." Sba: Controle & Automação Sociedade Brasileira de Automatica 17, no. 2 (June 2006): 189–204. http://dx.doi.org/10.1590/s0103-17592006000200007.
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In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite.
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Chandra, Kumar Pakki Bharani, Da-Wei Gu, and Ian Postlethwaite. "Cubature Kalman Filter based Localization and Mapping." IFAC Proceedings Volumes 44, no. 1 (January 2011): 2121–25. http://dx.doi.org/10.3182/20110828-6-it-1002.03104.
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Son, Hyesook, Van-Thanh Pham, Yun Jang, and Seung-Eock Kim. "Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network." Sensors 21, no. 9 (April 30, 2021): 3118. http://dx.doi.org/10.3390/s21093118.
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Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. In this paper, we propose a deep learning model that allows us to locate the damaged cable and estimate its cross-sectional area. To obtain the data required for the deep learning training, we use the tension data of the reduced area cable, which are simulated in the Practical Advanced Analysis Program (PAAP), a robust structural analysis program. We represent the sensor data of the damaged cable-stayed bridge as a graph composed of vertices and edges using tension and spatial information of the sensors. We apply the sensor geometry by mapping the tension data to the graph vertices and the connection relationship between sensors to the graph edges. We employ a Graph Neural Network (GNN) to use the graph representation of the sensor data directly. GNN, which has been actively studied recently, can treat graph-structured data with the most advanced performance. We train the GNN framework, the Message Passing Neural Network (MPNN), to perform two tasks to identify damaged cables and estimate the cable areas. We adopt a multi-task learning method for more efficient optimization. We show that the proposed technique achieves high performance with the cable-stayed bridge data generated from PAAP.
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Chen, Yuwei, Jian Tang, Changhui Jiang, Lingli Zhu, Matti Lehtomäki, Harri Kaartinen, Risto Kaijaluoto, et al. "The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies." Sensors 18, no. 10 (September 25, 2018): 3228. http://dx.doi.org/10.3390/s18103228.
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The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.
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Osborn, Joseph C., Adam Summerville, Nathan Dailey, and Soksamnang Lim. "MappyLand: Fast, Accurate Mapping for Console Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17, no. 1 (October 4, 2021): 66–73. http://dx.doi.org/10.1609/aiide.v17i1.18892.
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We present MappyLand, a rewrite and enhancement of the earlier Mappy automatic game mapping system, which leverages instrumentation of a game console emulator to produce, from a sequence of game inputs, accurate annotations for action/adventure games including object detection and tracking, in-game camera movement, grid-based tile maps, and links between identified disparate spaces. The overhead of generating these annotations is on the order of one millisecond per observed frame. We also show a higher latency (but still online) algorithm for merging together previous observations of distinct game maps for the purposes of agent localization across a long period of time. Specifically, our system can determine a consistent graph of game rooms from a set of strings of game rooms, capturing behaviors like backtracking and synthesizing observations from multiple play sessions. Altogether, this fast, accurate approach to mapping yields new and useful knowledge representations and expedient ways to produce new datasets given just a game and some example play.
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Wan, Jiuqing, Shaocong Bu, Jinsong Yu, and Liping Zhong. "Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772671. http://dx.doi.org/10.1177/1550147717726715.
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This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.
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Pradalier, Cédric, and Sepanta Sekhavat. "Simultaneous localization and mapping using the Geometric Projection Filter and correspondence graph matching." Advanced Robotics 17, no. 7 (January 2003): 675–90. http://dx.doi.org/10.1163/156855303769157018.
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Liu, Tian, Yongfu Chen, Zhiyong Jin, Kai Li, Zhenting Wang, and Jiongzhi Zheng. "Spare Pose Graph Decomposition and Optimization for SLAM." MATEC Web of Conferences 256 (2019): 05003. http://dx.doi.org/10.1051/matecconf/201925605003.
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The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction of the Single-chain and the Parallel-chain, the pose graph is decomposed into many small subgraphs. Compared with directly processing the original graph, the speed of calculation is accelerated by separately optimizing the subgraph, which is because the computational complexity is increasing exponentially with the increase of the graph’s scale. This method we proposed is very suitable for the current multi-threaded framework adopted in the mainstream SLAM, which separately calculate the subgraph decomposed by our method, rather than the original optimization requiring a large block of time in once may cause CPU obstruction. At the end of the paper, our algorithm is validated with the open source dataset of the mobile robot, of which the result illustrates our algorithm can reduce the one-time resource consumption and the time consumption of the calculation with the same map-constructing accuracy.
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Sheng, Bo, Chao Deng, Yao Xiong, Zhi Jun Luo, and Yuan Hang Wang. "Fault Diagnosis for CNC Machine Tool Based on Mapping Model." Applied Mechanics and Materials 607 (July 2014): 739–42. http://dx.doi.org/10.4028/www.scientific.net/amm.607.739.
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Since numerous parts and various faults of CNC machine tool, the mapping model is proposed to represent the complex relationship between faults. Then some matrices are used to process the mappings model. Finally, the priority of the fault sources is sorted by the fault localization algorithm based on the matrix above. Besides, the case of ram feed system of CNC boring machine tools FB260 illustrates the performance of mapping model based on fault diagnosis.
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Xia, Linlin, Jiashuo Cui, Ran Shen, Xun Xu, Yiping Gao, and Xinying Li. "A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142091918. http://dx.doi.org/10.1177/1729881420919185.
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As one of the typical application-oriented solutions to robot autonomous navigation, visual simultaneous localization and mapping is essentially restricted to simplex environmental understanding based on geometric features of images. By contrast, the semantic simultaneous localization and mapping that is characterized by high-level environmental perception has apparently opened the door to apply image semantics to efficiently estimate poses, detect loop closures, build 3D maps, and so on. This article presents a detailed review of recent advances in semantic simultaneous localization and mapping, which mainly covers the treatments in terms of perception, robustness, and accuracy. Specifically, the concept of “semantic extractor” and the framework of “modern visual simultaneous localization and mapping” are initially presented. As the challenges associated with perception, robustness, and accuracy are being stated, we further discuss some open problems from a macroscopic view and attempt to find answers. We argue that multiscaled map representation, object simultaneous localization and mapping system, and deep neural network-based simultaneous localization and mapping pipeline design could be effective solutions to image semantics-fused visual simultaneous localization and mapping.
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Wen, Weisong, Li-Ta Hsu, and Guohao Zhang. "Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong." Sensors 18, no. 11 (November 14, 2018): 3928. http://dx.doi.org/10.3390/s18113928.
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Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.
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Ali, Shimaa S., Abdallah Hammad, and Adly S. Tag Eldien. "Cloud-based map alignment strategies for multi-robot FastSLAM 2.0." International Journal of Distributed Sensor Networks 15, no. 3 (March 2019): 155014771982932. http://dx.doi.org/10.1177/1550147719829329.
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The cooperative simultaneous localization and mapping problem has acquired growing attention over the years. Even though mapping of very large environments is theoretically quicker than a single robot simultaneous localization and mapping, it has several additional challenges such as the map alignment and the merging processes, network latency, administering various coordinate systems and assuring synchronized and updated data from all robots and also it demands massive computation. This article proposes an efficient architecture for cloud-based cooperative simultaneous localization and mapping to parallelize its complex steps via the multiprocessor (computing nodes) and free the robots from all of the computation efforts. Furthermore, this work improves the map alignment part using hybrid combination strategies, random sample consensus, and inter-robot observations to exploit fully their advantages. The results show that the proposed approach increases mapping performance with less response time.
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Yang, Xin, Xiaohu Lin, Wanqiang Yao, Hongwei Ma, Junliang Zheng, and Bolin Ma. "A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation." Remote Sensing 15, no. 1 (December 29, 2022): 186. http://dx.doi.org/10.3390/rs15010186.
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Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection.