Affiliations: [a] State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning, China
| [b] School of Automotive Engineering, Dalian University of Technology, Dalian, Liaoning, China
Correspondence:
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Corresponding author. Linhui Li, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China. E-mail: lilinhui@dlut.edu.cn.
Note: [1] This work was supported in part by the National Natural Science Foundation of China under Grant 61976039, Grant 52172382, and in part by the Science and Technology Innovation Fund of Dalian under Grant 2021JJ12GX015, and in part by the China Fundamental Research Funds for the Central Universities under Grant DUT22JC09.
Abstract: Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation.
