A hybrid probabilistic and point set registration approach for fusion of 3D occupancy grid maps

Abstract

One of the major challenges in multi-robot exploration is to fuse the partial maps generated by individual robots into a consistent global map. We address 3D volumetric map fusion by extending the well known iterative closest point(ICP) algorithm to include probabilistic distance and surface information. In addition, the relative transformation is evaluated based on Mahalanobis distance and map dissimilarities are integrated using relative entropy filter. The efficiency of the proposed algorithm is evaluated using maps generated from both simulated and real environments and is shown to generate more consistent global maps.