Abstract
The research and application of lidar in the establishment of high-precision maps and path planning of intelligent driving vehicles are being developed as a technical support for intelligent driving technology. In this paper we mainly focus on the research and application of lidar real-time positioning and map building technology in intelligent driving vehicles, including the preprocessing of raw data required by Simultaneous Localization and Mapping (SLAM), the front-end algorithm of SLAM, the back-end optimization of SLAM and the final algorithm experiment. In the front-end algorithm of SLAM, we propose a boundary line feature extraction scheme based on the traditional curvature feature point extraction method and Random Sample Consensus (RANSAC) algorithm, and realize point cloud registration and pose estimation based on optimized Iterative Closest Point (ICP). In the back-end processing algorithm, we propose a sparse algorithm of graph nodes for selection, and propose boundary verification based on an improved loop detection and relocation strategy design. The results showed that the algorithm in this paper was less affected by noise, which considerably saves time in the operation of the loop frame determination algorithm and improves the safety of the intelligent vehicle in the process of driving.
Supported by the National Key R&D Program of China under Grant 2018YFB1105304, and National Natural Science Foundation of China under Grant U1664264, and National Natural Science Foundation of China under Grant U1864204.
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Wang, B., Han, Y., Jin, J. (2021). Research on Global Map Construction and Location of Intelligent Vehicles Based on Lidar. In: Cheng, J., Tang, X., Liu, X. (eds) Cyberspace Safety and Security. CSS 2020. Lecture Notes in Computer Science(), vol 12653. Springer, Cham. https://doi.org/10.1007/978-3-030-73671-2_19
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DOI: https://doi.org/10.1007/978-3-030-73671-2_19
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