Improved AMCL Relocation Algorithm based on Semantic Map with Corner Information

doi:10.3901/JME.2022.24.312

Abstract

Abstract: Aiming at the defects of the original AMCL positioning algorithm using only laser information, a semantic map based on laser and vision fusion is proposed for global positioning. The semantic map is fused with a target detection method based on deep learning to extract the corner semantics in the environment; use The established two-dimensional semantic grid map containing corner information, combined with the visual pre-positioning method and the semantic information table around the corner points to improve the efficiency and accuracy of the algorithm's global initial positioning, so that the mobile robot can be used in the situation of a small amount of prior information and motion To achieve positioning more quickly. The method of visual pre-positioning is proposed, the particle weight update method is improved, and the AMCL algorithm is combined with the environment map to perform precise positioning. Finally, a comparative experiment of the built mobile robot in different scenarios verifies the effectiveness of the method.

Key words: corner information, semantic information table, global pre-positioning, improved AMCL, semantic map

CLC Number:

TP249

JIANG Lin, NIE Wenkang, ZHU Jianyang, LIU Qi, TIAN Tixian, LI Jun. Improved AMCL Relocation Algorithm based on Semantic Map with Corner Information[J]. Journal of Mechanical Engineering, 2022, 58(24): 312-323.

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