This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Monocular Visual Localization for Autonomous Vehicles Based on Lightweight Landmark Map
Technical Paper
2022-01-7094
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Vehicle pose estimation is a key technology for autonomous vehicles and a prerequisite for path planning and vehicle control. Visual localization has gradually attracted extensive attention from academia and industry due to its low cost and rich semantic information. However, the incremental calculation principle of the odometry inevitably leads to the accumulation of localization error with the travel distance. To solve this problem, we propose a position correction algorithm based on lightweight landmark map, and further compensate the localization error by analyzing the error characteristics. The proposed algorithm takes the stop lines on the road as landmarks, and pairs bag-of-word vectors with the positions of the corresponding landmarks. Once landmarks in the map are encountered and successfully associated, the position of the landmarks can be exploited to effectively reduce the drift of the odometry. We also present a reliable landmark map construction method. Experiments show that with only one monocular camera and the established landmark map, the proposed algorithm can significantly reduce the cumulative error and achieve decimeter-level positioning accuracy, which meets the lane-level positioning requirements of autonomous vehicles driving long distances under fixed routes.
Authors
Topic
Citation
Zhuo, G., Fu, W., and Xue, F., "Monocular Visual Localization for Autonomous Vehicles Based on Lightweight Landmark Map," SAE Technical Paper 2022-01-7094, 2022, https://doi.org/10.4271/2022-01-7094.Also In
References
- Shan , T. et al. Lio-sam: Tightly-Coupled Lidar Inertial Odometry via Smoothing and Mapping 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
- Qin , T. et al. A General Optimization-Based Framework for Local Odometry Estimation with Multiple Sensors 2019
- Zhang , J. and Singh , S. Visual-Lidar Odometry and Mapping: Low-Drift, Robust, and Fast 2015 IEEE International Conference on Robotics and Automation (ICRA) 2015
- Wisth , D. et al. Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry IEEE Robotics and Automation Letters 6 2 2021 1004 1011
- Mur-Artal , R. and Tardós , J.D. Orb-slam2: An Open-Source Slam System for Monocular, Stereo, and RGB-D Cameras IEEE Transactions on Robotics 33 5 2017 1255 1262
- Wang , R. , Schworer , M. , and Cremers , D. Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras Proceedings of the IEEE International Conference on Computer Vision 2017
- Bénet , P. and Guinamard , A. Robust and Accurate Deterministic Visual Odometry Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) 2020
- Yang , N. et al. D3vo: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020
- Gao , X. et al. LDSO: Direct Sparse Odometry with Loop Closure 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018
- Shan , T. et al. Lvi-sam: Tightly-Coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021
- Shao , W. et al. Stereo Visual Inertial Lidar Simultaneous Localization and Mapping 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019
- Gomez-Ojeda , R. et al. PL-SLAM: A Stereo SLAM System through the Combination of Points and Line Segments IEEE Transactions on Robotics 35 3 2019 734 746
- Wang , T. and Ling , H. Gracker: A Graph-Based Planar Object Tracker IEEE Transactions on Pattern Analysis and Machine Intelligence 40 6 2017 1494 1501
- Asghar , R. et al. Vehicle Localization Based on Visual Lane Marking and Topological Map Matching 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020
- Engel , J. , Koltun , V. , and Cremers , D. Direct Sparse Odometry IEEE Transactions on Pattern Analysis and Machine Intelligence 40 3 2017 611 625
- Xue , F. et al. Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
- Canny , J. A Computational Approach to Edge Detection IEEE Transactions on Pattern Analysis and Machine Intelligence 6 1986 679 698
- Ballard , D.H. Generalizing the Hough Transform to Detect Arbitrary Shapes Pattern Recognition 13 2 1981 111 122
- Gálvez-López , D. and Tardos , J.D. Bags of Binary Words for Fast Place Recognition in Image Sequences IEEE Transactions on Robotics 28 5 2012 1188 1197
- Rublee , E. et al. ORB: An Efficient Alternative to SIFT or SURF 2011 International Conference on Computer Vision 2011
- Campos , C. et al. Orb-slam3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap Slam IEEE Transactions on Robotics 37 6 2021 1874 1890
- Shan , T. and Englot , B. Lego-Loam: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018