Improved AMCL Relocation Algorithm based on Semantic Map with Corner Information

JIANG Lin; NIE Wenkang; ZHU Jianyang; LIU Qi; TIAN Tixian; LI Jun

doi:10.3901/JME.2022.24.312

Volume 58 Issue 24

Dec 2022

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JOURNAL OF MECHANICAL ENGINEERING > 2022 > 58(24): 312-323.

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. doi: 10.3901/JME.2022.24.312

Citation:

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. doi: 10.3901/JME.2022.24.312

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Improved AMCL Relocation Algorithm based on Semantic Map with Corner Information

doi: 10.3901/JME.2022.24.312

JIANG Lin^{1, 2
,},
NIE Wenkang^1
,,
ZHU Jianyang^{1, 2,*
,
,},
LIU Qi^1
,,
TIAN Tixian^{1, 2
,},
LI Jun^1
,

1.
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081
2.
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081

Received Date: 10 Mar 2022
Rev Recd Date: 23 Oct 2022

Available Online: 07 Mar 2024

Issue Publish Date: 20 Dec 2022

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.
- corner information,
- semantic information table,
- global pre-positioning,
- improved AMCL,
- semantic map

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References(21)

References

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