ABSTRACT
Accurate and reliable localization is necessary for vehicle autonomous driving. Existing localization systems based on the GNSS cannot always provide lane-level accuracy. This paper proposes the method that improves vehicle localization by using road lanes recognized from a camera and a digital map. Iterative Closest Point (ICP) matching is performed for generated point clouds to minimize lateral error. The neural network is used for lane detection, detections are post-processed and fitted to the polynomial. Changes that allowed improving ICP matching are described. Finally, we perform an experiment with GPS RTK signal as ground truth and demonstrate that the proposed method has a position error of less than 0.5 m for vehicle localization.
- Rabbia Asghar, Mario Garzón, Jerome Lussereau, and Christian Laugier. 2019. Map Relative Localization Based on Visual and Topological Map Matching. Technical Report. Inria Chroma. https://hal.inria.fr/hal-02412837Google Scholar
- Alexander Buyval, Aidar Gabdullin, Salimzhan Gafurov, Roman Fedorenko, and Maxim Lyubimov. 2019. The Architecture of the Self-Driving Car Project at Innopolis University. 504--509. https://doi.org/10.1109/DeSE.2019.00098Google Scholar
- Alexander Buyval, Aidar Gabdullin, and Maxim Lyubimov. 2019. Road sign detection and localization based on camera and lidar data. 101. https://doi.org/10.1117/12.2523155Google Scholar
- Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, and Juan D. Tardós. 2020. ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM. arXiv:2007.11898 [cs.RO]Google Scholar
- M.J. Choi, J.K. Suhr, K. Choi, and H.G. Jung. 2019. Low-Cost Precise Vehicle Localization Using Lane Endpoints and Road Signs for Highway Situations. IEEE Access 7 (2019), 149846--149856.Google ScholarCross Ref
- Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarDigital Library
- Jeong Min Kang, Tae Sung Yoon, Euntai Kim, and Jin Bae Park. 2020. Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map. Sensors 20, 8 (Apr 2020), 2166. https://doi.org/10.3390/s20082166Google ScholarCross Ref
- Y. Kim and D. Kum. 2019. Deep Learning based Vehicle Position and Orientation Estimation via Inverse Perspective Mapping Image. In 2019 IEEE Intelligent Vehicles Symposium (IV). 317--323.Google Scholar
- F. Li, P. Bonnifait, and J. Ibañez-Guzmán. 2018. Map-Aided Dead-Reckoning With Lane-Level Maps and Integrity Monitoring. IEEE Transactions on Intelligent Vehicles 3, 1 (2018), 81--91.Google ScholarCross Ref
- National Academy of Engineering. 2020. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2019 Symposium. The National Academies Press, Washington, DC, 67--74. https://doi.org/10.17226/25620Google Scholar
- Vladislav Ostankovich and Rauf Yagfarov. 2020. Segmification: Solving road segmentation and scene classification tasks for self-driving cars using one neural network. 1--5. https://doi.org/10.1145/3378184.3378190Google ScholarDigital Library
- F. Poggenhans, N.o. Salscheider, and. Stiller. 2018. Precise Localization in High-Definition Road Maps for Urban Regions. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 317--323.Google Scholar
- T. Shan and B. Englot. 2018. LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4758--4765.Google Scholar
- S. Verentsov, E. Magerramov, V. Vinogradov, R. Gizatullin, A. Alekseenko, Y. Kholodov, and E. Nikolskiy. 2018. Bayesian Framework for Vehicle Localization Using Crowdsourced Data. In 2018 IEEE Intelligent Vehicles Symposium (IV). 215--219.Google Scholar
- Guowei Wan, Xiaolong Yang, Renlan Cai, Hao Li, Hao Wang, and Shiyu Song. 2017. Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes. CoRR abs/1711.05805 (2017). arXiv:1711.05805 http://arxiv.org/abs/1711.05805Google Scholar
Index Terms
- Map relative localization based on road lane matching with Iterative Closest Point algorithm
Recommendations
Map-Matching-Based Localization Using Camera and Low-Cost GPS For Lane-Level Accuracy
AbstractFor self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a necessity for performing complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have lane-level localization to ...
On the cooperative automatic lane change: speed synchronization and automatic "courtesy"
DATE '17: Proceedings of the Conference on Design, Automation & Test in EuropeThe recent ability of some vehicles to handle autonomously the lane change maneuvers, and the progressive equipment of roads and vehicles with ITS-G5 units motivate this paper to consider the case of road narrowing that requires a lane change because ...
Probabilistic lane estimation for autonomous driving using basis curves
Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and ...
Comments