An algorithm of simultaneous localization and mapping for mobile robots based on graph optimization

Kun Wei; Xiangyu Zheng; Wenqing Wei; Mengjun Song; Jinhua Zhang; Haiyun Gan; Yuan Guo; Jianhui Zhang

doi:10.1117/12.3029720

21 June 2024 An algorithm of simultaneous localization and mapping for mobile robots based on graph optimization

Kun Wei, Xiangyu Zheng, Wenqing Wei, Mengjun Song, Jinhua Zhang, Haiyun Gan, Yuan Guo, Jianhui Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670O (2024) https://doi.org/10.1117/12.3029720
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Simultaneous Localization and Mapping (SLAM) is a core technology for autonomous driving of intelligent vehicles. Based on the single-sensor mapping and localization method, it cannot tackle the issues of environmental degradation and low sampling frequency, and the accuracy and robustness of the mapping and localization solutions, which needs to be further improved. With the development of Multi-sensor Fusion methods, it is possible to make full use of the complementary performance advantages of multiple sensors to fuse the localization and mapping solutions of multiple sensors, which can better meet the requirement of high-precision localization in most scenarios and enhance the robustness of the algorithm. Therefore, this paper proposes a SLAM algorithm for mobile robots based on graph optimization. Due to the large workload of the Cartographer algorithm for adjusting parameters, an optimization method is designed, and a suitable range of parameter values is processed according to this method. With the aim of improving map building accuracy and localization stability, a detection method of Scan Context fusion history frame closed-loop is proposed to improve the closed-loop detection accuracy in the LIO-SAM algorithm. Finally, mobile carts of indoor and outdoor are established in real scenarios. The paper shows that the Cartographer parameter optimization scheme is feasible and the Scan Context closed-loop detection algorithm is more robust, which improves the autonomous localization accuracy of the mobile robot in indoor and outdoor environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kun Wei, Xiangyu Zheng, Wenqing Wei, Mengjun Song, Jinhua Zhang, Haiyun Gan, Yuan Guo, and Jianhui Zhang "An algorithm of simultaneous localization and mapping for mobile robots based on graph optimization", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670O (21 June 2024); https://doi.org/10.1117/12.3029720

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KEYWORDS

Detection and tracking algorithms

Mathematical optimization

Point clouds

Mobile robots

LIDAR

Algorithm development

Matrices

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