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Occupancy grids are the most common framework when it comes to creating a mapof the environment using a robot. This paper studies occupancy grids from themotion planning perspective and proposes a mapping method that provides richerdata (map) for the purpose of planning and collision avoidance. Typically, inoccupancy grid mapping, each cell contains a single number representing theprobability of cell being occupied. This leads to conflicts in the map, andmore importantly inconsistency between the map error and reported confidencevalues. Such inconsistencies pose challenges for the planner that relies on thegenerated map for planning motions. In this work, we store a richer data ateach voxel including an accurate estimate of the variance of occupancy. We showthat in addition to achieving maps that are often more accurate than traditionmethods, the proposed filtering scheme demonstrates a much higher level ofconsistency between its error and its reported confidence. This allows theplanner to reason about acquisition of the future sensory information. Suchplanning can lead to active perception maneuvers that while guiding the robottoward the goal aims at increasing the confidence in parts of the map that arerelevant to accomplishing the task.

SMAP: Simultaneous Mapping and Planning on Occupancy Grids