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Zhu Y, An H, Wang H, Xu R, Wu M, Lu K. RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization. SENSORS (BASEL, SWITZERLAND) 2024; 24:536. [PMID: 38257631 PMCID: PMC11154416 DOI: 10.3390/s24020536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/18/2023] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.
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Affiliation(s)
- Yuan Zhu
- School of Automotive Studies, Tongji University, Shanghai 201800, China; (Y.Z.); (H.A.); (H.W.); (R.X.)
| | - Hao An
- School of Automotive Studies, Tongji University, Shanghai 201800, China; (Y.Z.); (H.A.); (H.W.); (R.X.)
| | - Huaide Wang
- School of Automotive Studies, Tongji University, Shanghai 201800, China; (Y.Z.); (H.A.); (H.W.); (R.X.)
| | - Ruidong Xu
- School of Automotive Studies, Tongji University, Shanghai 201800, China; (Y.Z.); (H.A.); (H.W.); (R.X.)
| | - Mingzhi Wu
- Nanchang Automotive Institute of Intelligence & New Energy, Tongji University, Nanchang 330038, China;
| | - Ke Lu
- School of Automotive Studies, Tongji University, Shanghai 201800, China; (Y.Z.); (H.A.); (H.W.); (R.X.)
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2
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Yang C, Xiong Z, Liu J, Chao L, Chen Y. A Path Integration Approach Based on Multiscale Grid Cells for Large-Scale Navigation. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3092609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Chuang Yang
- Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zhi Xiong
- Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jianye Liu
- Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Lijun Chao
- Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Yudi Chen
- Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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3
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Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01582-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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4
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Xu Z, Rong Z, Wu Y. A survey: which features are required for dynamic visual simultaneous localization and mapping? Vis Comput Ind Biomed Art 2021; 4:20. [PMID: 34269925 PMCID: PMC8285453 DOI: 10.1186/s42492-021-00086-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/10/2021] [Indexed: 11/10/2022] Open
Abstract
In recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.
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Affiliation(s)
- Zewen Xu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheng Rong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yihong Wu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
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5
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Krátký V, Petráček P, Báča T, Saska M. An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environments. J FIELD ROBOT 2021. [DOI: 10.1002/rob.22021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Vít Krátký
- Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University in Prague Praha Czech Republic
| | - Pavel Petráček
- Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University in Prague Praha Czech Republic
| | - Tomáš Báča
- Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University in Prague Praha Czech Republic
| | - Martin Saska
- Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University in Prague Praha Czech Republic
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6
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A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars. REMOTE SENSING 2021. [DOI: 10.3390/rs13102015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy.
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7
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Tomasi J, Wagstaff B, Waslander SL, Kelly J. Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3058909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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8
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Wang K, Ma S, Chen J, Ren F, Lu J. Approaches Challenges and Applications for Deep Visual Odometry Toward to Complicated and Emerging Areas. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.3038898] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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9
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Kong W, Yang M, Huang Q. Hilbert stereo reconstruction algorithm based on depth feature and stereo matching. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-201749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance.
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Affiliation(s)
- Weiyi Kong
- Electrical Information Engineering, Southwest Minzu University, Chengdu, Sichuan, China
| | - Menglong Yang
- Aeronautics and Astronauticst, Sichuan University, Chengdu, Sichuan, China
| | - Qinzhen Huang
- Electrical Information Engineering, Southwest Minzu University, Chengdu, Sichuan, China
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10
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Abstract
Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.
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11
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Fayyad J, Jaradat MA, Gruyer D, Najjaran H. Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4220. [PMID: 32751275 PMCID: PMC7436174 DOI: 10.3390/s20154220] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 01/31/2023]
Abstract
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.
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Affiliation(s)
- Jamil Fayyad
- School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada;
| | - Mohammad A. Jaradat
- Department of Mechanical Engineering, American University of Sharjah, Sharjah, UAE;
- Department of Mechanical Engineering, Jordan University of Science & Technology, Irbid 22110, Jordan
| | - Dominique Gruyer
- PICS-L, COSYS, University Gustave Eiffel, IFSTTAR, 25 allée des Marronniers, 78000 Versailles, France;
| | - Homayoun Najjaran
- School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada;
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12
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Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment. REMOTE SENSING 2020. [DOI: 10.3390/rs12101564] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.
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13
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Zuo X, Ye W, Yang Y, Zheng R, Vidal‐Calleja T, Huang G, Liu Y. Multimodal localization: Stereo over LiDAR map. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21936] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Xingxing Zuo
- Institute of Cyber‐Systems and Control, College of Control Science and EngineeringZhejiang UniversityHangzhou China
| | - Wenlong Ye
- Institute of Cyber‐Systems and Control, College of Control Science and EngineeringZhejiang UniversityHangzhou China
| | - Yulin Yang
- Department of Mechanical EngineeringUniversity of DelawareNewark Delaware
| | - Renjie Zheng
- Institute of Cyber‐Systems and Control, College of Control Science and EngineeringZhejiang UniversityHangzhou China
| | - Teresa Vidal‐Calleja
- Centre for Autonomous Systems, School of Mechanical and Mechatronic EngineeringUniversity of Technology SydneySydney New South Wales Australia
| | - Guoquan Huang
- Department of Mechanical EngineeringUniversity of DelawareNewark Delaware
| | - Yong Liu
- Institute of Cyber‐Systems and Control, College of Control Science and EngineeringZhejiang UniversityHangzhou China
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14
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Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10020698] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources.
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15
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Li X, Du S, Li G, Li H. Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping. SENSORS 2019; 20:s20010237. [PMID: 31906166 PMCID: PMC6982927 DOI: 10.3390/s20010237] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/22/2019] [Accepted: 12/27/2019] [Indexed: 11/16/2022]
Abstract
Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.
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16
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An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments. SENSORS 2019; 19:s19184025. [PMID: 31540461 PMCID: PMC6766956 DOI: 10.3390/s19184025] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 09/13/2019] [Accepted: 09/16/2019] [Indexed: 11/25/2022]
Abstract
In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m.
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17
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Kechagias‐Stamatis O, Aouf N, Dubanchet V. Evaluating 3D local descriptors and recursive filtering schemes for LIDAR‐based uncooperative relative space navigation. J FIELD ROBOT 2019. [DOI: 10.1002/rob.21904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Odysseas Kechagias‐Stamatis
- Signals and Autonomy Group, Centre for Electronic Warfare, Information and Cyber, Cranfield Defence and Security Cranfield University Shrivenham UK
- Department of Electrical and Electronic Engineering University of London London UK
| | - Nabil Aouf
- Department of Electrical and Electronic Engineering University of London London UK
| | - Vincent Dubanchet
- Department of Space Robotics and GNC/AOCS Thales Alenia Space Cannes France
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18
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Shin YS, Kim A. Sparse Depth Enhanced Direct Thermal-Infrared SLAM Beyond the Visible Spectrum. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2923381] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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19
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Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences. SENSORS 2019; 19:s19092165. [PMID: 31075978 PMCID: PMC6539904 DOI: 10.3390/s19092165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 05/08/2019] [Accepted: 05/08/2019] [Indexed: 11/17/2022]
Abstract
A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.
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20
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Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization. REMOTE SENSING 2019. [DOI: 10.3390/rs11010067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.
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21
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Labbé M, Michaud F. RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J FIELD ROBOT 2018. [DOI: 10.1002/rob.21831] [Citation(s) in RCA: 259] [Impact Index Per Article: 43.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Mathieu Labbé
- Department of Electrical Engineering and Computer Engineering; Interdisciplinary Institute of Technological Innovation (3IT), Université de Sherbrooke; Sherbrooke Québec Canada
| | - François Michaud
- Department of Electrical Engineering and Computer Engineering; Interdisciplinary Institute of Technological Innovation (3IT), Université de Sherbrooke; Sherbrooke Québec Canada
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