Robust Loop Closure Selection Based on Inter-Robot and Intra-Robot Consistency for Multi-Robot Map Fusion.

In multi-robot simultaneous localization and mapping (SLAM) systems, the system must create a consistent global map with multiple local maps and loop closures between robot poses. However, false-positive loop closures caused by perceptual aliasing can severely distort the global map, especially in GNSS-denied areas, where a good prior of relative poses between robots is unavailable. In addition, the performance of the consistency metric in existing map fusion methods relies on accurate odometry from each robot. However, in practice, cumulative noise is inevitably present in robot trajectories, which leads to poor map fusion with existing methods. Thus, in this paper, we propose a robust consistency-based inter-robot and intra-robot loop closure selection algorithm for map fusion. We consider both pairwise-loop consistency and loop-odometry consistency to improve robustness against false-positive loop closures and accumulative noise in the odometry. Specifically, we select a reliable inter-robot loop closure measurement with a consistency-based strategy to provide an initial prior of relative pose between two robot trajectories and update the pose variables of the robot trajectories. The loop closure selection problem is formulated as a maximum edge weight clique problem in graph theory. A performance evaluation of the proposed method was conducted on the ManhattanOlson3500, modified CSAIL and Bicocca datasets, and the experimental results demonstrate that the proposed method outperforms the pairwise consistency measurement set maximization method (PCM) under severe accumulative noise and can be integrated with M-estimation methods.