Autonomous Localisation Method Based on Linear Feature for Robots

doi:10.16183/j.cnki.jsjtu.2018.09.017

Abstract

Abstract: The convergence for iterative closest point (ICP) algorithm depends much on the inputs, which often results in local optimum. And normal distribution transform (NDT) algorithm usually acquires more scans in scan matching, and the large angular deviation occurs during long-term autonomous navigation for robots. An improved ICP self-localization algorithm based on line feature is proposed in the paper. We derive functional relations of covariance matrix between matching scans and their fitting lines to scan and match line features rapidly. And the robot updates global poses in terms of closed form correlations. We test the reliability of CICP algorithm in indoor locating experiments. The experiments show that CICP algorithm, compared with ICP or NDT, owns higher real-time and location accuracy, and can reduce matrix computation and improve self-localization precision efficiently.

Key words: autonomous robots, global localization, iterative closest point (ICP), scan matching, filtering

CLC Number:

TP 242

YANG Jingdong,PENG Kun,GU Haonan,SHI Yanwei. Autonomous Localisation Method Based on Linear Feature for Robots[J]. Journal of Shanghai Jiaotong University, 2018, 52(9): 1120-1124.

References

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