Confidence-rich Grid Mapping

Occupancy grids are common frameworks for creating a map of the environment using a robot. Traditional occupancy grid mapping algorithms assume that map voxels are independent of each other. In addition, they utilize a map representation, wherein each voxel stores a single number representing the occupancy probability. This leads to conflicts in the map, and more importantly, inconsistency between the map error and the reported confidence values. Such inconsistencies pose challenges for planners that rely on the generated map for collision avoidance. This paper studies occupancy grids from a planning perspective and proposes a new grid mapping algorithm in the presence of noisy measurements. In our novel representation of the map, we store richer data at each voxel, which includes an accurate estimate of the variance of occupancy. We show that in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence.