다중 카메라 시스템을 위한 전방위 Visual-LiDAR SLAM
Omni-directional Visual-LiDAR SLAM for Multi-Camera System
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Due to the limited field of view of the pinhole camera, there is a lack of stability and accuracy in camera pose estimation applications such as visual SLAM. Nowadays, multiple-camera setups and large field of cameras are used to solve such issues. However, a multiple-camera system increases the computation complexity of the algorithm. Therefore, in multiple camera-assisted visual simultaneous localization and mapping (vSLAM) the multi-view tracking algorithm is proposed that can be used to balance the budget of the features in tracking and local mapping. The proposed algorithm is based on PanoSLAM architecture with a panoramic camera model. To avoid the scale issue 3D LiDAR is fused with omnidirectional camera setup. The depth is directly estimated from 3D LiDAR and the remaining features are triangulated from pose information. To validate the method, we collected a dataset from the outdoor environment and performed extensive experiments. The accuracy was measured by the absolute trajectory error which shows comparable robustness in various environments.
Abstract
Due to the limited field of view of the pinhole camera, there is a lack of stability and accuracy in camera pose estimation applications such as visual SLAM. Nowadays, multiple-camera setups and large field of cameras are used to solve such issues. However, a multiple-camera system increases the computation complexity of the algorithm. Therefore, in multiple camera-assisted visual simultaneous localization and mapping (vSLAM) the multi-view tracking algorithm is proposed that can be used to balance the budget of the features in tracking and local mapping. The proposed algorithm is based on PanoSLAM architecture with a panoramic camera model. To avoid the scale issue 3D LiDAR is fused with omnidirectional camera setup. The depth is directly estimated from 3D LiDAR and the remaining features are triangulated from pose information. To validate the method, we collected a dataset from the outdoor environment and performed extensive experiments. The accuracy was measured by the absolute trajectory error which shows comparable robustness in various environments.
참고문헌 (20)
- Guillaume Bresson, "Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving", IEEE Transactions on Intelligent Vehicles, 2(3), pp.194~220 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Cesar Cadena, "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age", IEEE Transactions on Robotics, 32(6), pp.1309~1332 (2016), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Raul Mur-Artal, "ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras", IEEE Transactions on Robotics, 33(5), pp.1255~1262 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- J. Engel, "LSD-SLAM : Large-scale direct monocular SLAM", European Conference on Computer Vision, pp.834~849 (2014)
- G. Klein, "Parallel tracking and mapping for small AR workspaces", 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (2007)
- Jakob Engel, "Direct Sparse Odometry", IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), pp.611~625 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- C. Forster, "SVO : Fast semi-direct monocular visual odometry", 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014)
- Hao Chen, "PALVO: visual odometry based on panoramic annular lens", Optics Express, 27(17), pp.24481~ (2019), The Optical Society[DOI]
- D. Scaramuzza, "Monocular omnidirectional visual odometry for outdoor ground vehicles", International Conference on Computer Vision Systems, pp.206~215 (2008)
- P. Liu, "Direct visual odometry for a fisheye-stereo camera", 2017 IEEE, 2017, ~
- Shunping Ji, "Panoramic SLAM from a multiple fisheye camera rig", ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp.169~183 (2020), Elsevier BV[DOI]
- Yi Yang, "Multi-camera visual SLAM for off-road navigation", Robotics and Autonomous Systems, 128, pp.103505~ (2020), Elsevier BV[DOI]
- C. Won, "OmniSLAM:Omnidirectional Localization and Dense Mapping for Widebaseline Multi-camera Systems", 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Y. Wang, "CubemapSLAM : A Piecewise-Pinhole Monocular Fisheye SLAM System", Asian Conference on Computer Vision, pp.34~49 (2018)
- S. Urban, "MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System", arXiv:1610.07336, ~ (2016)
- G. Pandey, "Automatic Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information", Twenty-Sixth AAAI Conference on 2012
- G. Pandey, "Ford Campus vision and lidar data set", Sage Journals, 30(13) (2011)[DOI]
- Zeeshan Javed, "PanoVILD: a challenging panoramic vision, inertial and LiDAR dataset for simultaneous localization and mapping", The Journal of Supercomputing, 78(6), pp.8247~8267 (2022), Springer Science and Business Media LLC[DOI]
- R. Kümmerle, "G2o : A general framework for graph optimization", 2011IEEE International Conference on Robotics and Automation (ICRA) (2011)
- A. Angeli, "Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words", IEEE Transactions on Robotics, 24(5), pp.1027~1037 (2008), Institute of Electrical and Electronics Engineers (IEEE)[DOI]