Efficient Iterative Closest Point Localisation Algorithm for Autonomous Robots

doi:10.3901/JME.2018.15.015

Abstract

Abstract: The iterative closest point (ICP) algorithm can achieve good performance in the registration of laser scans to some extent. But the convergence depends much on the inputs. It is easy to lead to the local optimal. Normal distribution transform (NDT) algorithm has a higher precision than ICP sometimes, but a large amount of scans are needed for NDT algorithm in the process of matching. The larger angular deviation will happen during long-term autonomous navigation for mobile robots. An improved ICP algorithm is proposed. The error model of the feature matched scans basing on ICP algorithm is derived. The multiple data filters are applied to the features match to reduce the noises of the scans and speed up converge. Also the pose update algorithms are derived to improve the accuracy of global localization after filtering. The experiments show that FICP algorithm, compared with ICP or NDT, has a higher localization accuracy and a better real-time.

Key words: filtering, global localization, iterative closest point, scan matching

CLC Number:

TG156

YANG Jingdong, SUN Leiming, SHAO Yujie, SHI Yanwei. Efficient Iterative Closest Point Localisation Algorithm for Autonomous Robots[J]. Journal of Mechanical Engineering, 2018, 54(15): 15-20,30.

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