Efficient distributed multi-robot SLAM under missed detections and clutter with labeled multi-Bernoulli map

The labeled multi-Bernoulli (LMB) propagation of the environmental map has demonstrated its effectiveness for single-vehicle simultaneous localization and mapping (SLAM), yielding the LMB-SLAM method. In applications of LMB-SLAM, the robot is equipped with sensors (such as radar) that provide detection points of landmarks, so that the main challenge of performing SLAM is to tackle data association between landmarks and measurements under missed detections and clutter. This paper provides two contributions on LMB-SLAM. First, we improve its computational efficiency by an appropriate design of the proposal distribution that approximates the posterior of the vehicle pose. In this way, particles can be sampled in a more efficient way thus remarkably reducing their number, and the consequent computational load required to achieve a given performance level. Secondly, we extend LMB-SLAM to the multi-vehicle case, assuming that relative initial poses among vehicles are known, by suitable design of a map fusion method that allows to generate a more accurate and complete map in both explored and unexplored regions so as to improve both localization and mapping performance by vehicle cooperation. The improved performance of the proposed, single-vehicle and multi-vehicle, LMB-SLAM algorithms is assessed via experiments on both simulated and real data.