Visual localization and mapping based on road constraints

Jingchun Wu; Tao Wu; Pingmei Shi

doi:10.1117/12.2683855

14 June 2023 Visual localization and mapping based on road constraints

Jingchun Wu, Tao Wu, Pingmei Shi

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127081H (2023) https://doi.org/10.1117/12.2683855
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

Conventional SLAM only requires the construction of sparse maps for localization, while in order to meet the safe driving needs of unmanned vehicles, they need to understand the edges of the road, i.e., with a semantic level of understanding. In addition, unmanned vehicles are more sensitive to lateral errors than longitudinal errors, which requires SLAM algorithms with higher accuracy for lateral errors. We investigate the ORB-SLAM3 algorithm by introducing satellite maps as a priori knowledge, using the corners in satellite maps to initialize the odometer, remove the accumulated errors, and correct the previous positions; using the results of particle filtering and lane line identification to further optimize the localization results of ORB-SLAM3 and to draw maps with lane semantic information. Our experiments show that our algorithm significantly reduces the cumulative error without loopback, improves the localization accuracy, and yields lane line maps with large engineering applications.

Citation Download Citation

Jingchun Wu, Tao Wu, and Pingmei Shi "Visual localization and mapping based on road constraints", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127081H (14 June 2023); https://doi.org/10.1117/12.2683855

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