Robust submap-based probabilistic inconsistency detection for multi-robot mapping

The primary goal of employing multiple robots in active mapping tasks is to generate a globally consistent map efficiently. However, detecting the inconsistency of the generated global map is still an open problem. In this paper, a novel multi-level approach is introduced to measure the full 3D map inconsistency in which submap-based tests are performed at both single robot and multi-robot level. The conformance test based on submaps is done by modeling the histogram of the misalignment error metric into a truncated Gaussian distribution. Besides, the detected inconsistency is further validated through a 3D map registration process. The accuracy of the proposed method is evaluated using submaps from challenging environments in both indoor and outdoor, which illustrates its usefulness and robustness for multi-robot mapping tasks.