The most prominent example of scan matching algorithm is the Iterative Closest Point (ICP) algorithm. But the ICP algorithm and its variants excessively depend on the initial pose estimate between two scans. The purpose of this paper is to propose a scan matching algorithm, which is adaptable to big initial pose errors.
The environments are represented by flat units and upright units. The upright units are clustered to represent objects that the robot cannot cross over. The object cluster is further discretized to generate layered model consisting of cross-section ellipses. The layered model provides simplified features that facilitate an object recognition algorithm to discriminate among common objects in outdoor environments. A layered model graph is constructed with the recognized objects as nodes. Based on the similarity of sub-graphs in each scans, the layered model graph-based matching algorithm generates initial pose estimates and uses ICP to refine the scan matching results.
Experimental results indicate that the proposed algorithm can deal with bad initial pose estimates and increase the processing speed. Its computation time is short enough for real-time implementation in robotic applications in outdoor environments.
This paper proposes a bio-inspired scan matching algorithm for mobile robots based on layered model graph in outdoor environments.
This work has been supported by National Natural Science Foundation of China (Grant No. 61503056, 61305103), State Key Laboratory of Robotics and Systems (HIT) (Grant No. SKLRS-2015-MS-07) and Researches on the fundamental problems of next generation human-collaborated industrial robots (Grant No. U1508208).
Yan, F., Wang, K., Xiao, J. and Li, R. (2016), "A bio-inspired scan matching algorithm for mobile robots in outdoor environments", Assembly Automation, Vol. 36 No. 2, pp. 159-171. https://doi.org/10.1108/AA-11-2015-103Download as .RIS
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