Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity

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For an efficient collaboration of multi-robot system during missions, it is essential for the system to create a global map and localize the robots in it. However, the relative poses among robots may be unknown, preventing the system from generating the reference map. In such cases, the necessary information must be inferred through inter-robot loop closures, which are mainly perception-derived measurements obtained when robots observe the same place. However, as perception-derived measurements rely on the similarity of sensor data, different places could be wrongly identified as the same location if they exhibit similar appearances. This phenomenon, called perceptual aliasing, produces inaccurate loop closures that can severely distort the global map. This study presents a robust inter-robot loop closure selection for map fusion that utilizes the degrees of both consistency and data similarity of the loop closures for accurate measurement determination. We define the coalition of these information as the measurement pair score and employ it as weights in the objective function of the combinatorial optimization problem that can be solved as maximum edge weight clique from graph theory. The algorithm is tested on an experimental dataset for performance evaluation and the result is discussed in comparison to a state-of-the-art method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-10
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.5, no.4, pp.5701 - 5708

ISSN
2377-3766
DOI
10.1109/lra.2020.3010731
URI
http://hdl.handle.net/10203/275894
Appears in Collection
ME-Journal Papers(저널논문)
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