Detection and Filtering of Landmark Occlusions using Terrain Spatiograms

A team of robots cooperating to quickly produce a map needs to share landmark information between members so that the local maps can be accurately merged. However, the appearance of landmarks as seen by members of the team can change dramatically due to the phenomenon of occlusion. We have previously presented an approach to landmark representation using Terrain Spatiograms – an extension to image spatiograms in which the spatial information relates to the scene rather than the image. Because this representation preserves depth structure, it is possible to identify and filter potential occlusions. We present an approach to identifying and filtering occlusions using terrain spatiograms, and report experimental results on 20 landmark datasets for varying states of occlusion. We show that occlusion can be detected and filtered, resulting in improved landmark matching scores