Simultaneous Merging Multiple Grid Maps Using the Robust Motion Averaging
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Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that the proposed approach can achieve simultaneous merging of multiple grid maps with good performances.
KeywordsMulti-robot systems Grid map merging Iterative closet point Image registration Motion averaging
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This work is supported by the National Natural Science Foundation of China under Grant nos. 61573273 and 91648121, the Natural Science Foundation of Shaanxi Province of China under Grant no. 2015JM6301, the Fundamental Research Funds for Central Universities under Grant No. xjj2018214.
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