Probabilistic occlusion estimation in cluttered environments for active perception planning

This paper presents a probabilistic framework for scene modeling and active perception planning in complex environments. It tackles the problems of representing detection and transition uncertainties in multi-object scenes without knowledge of the total number of objects in the scenario. The correct association of observation data with scene information is essential for reasonable incorporation of sequencing measurements into the scene model. This work also deals with the probabilistic computation of object occlusions for probabilistic action planning in order to select the most profitable prospective viewpoint. Concepts from computer graphics and statistics are combined for the efficient and precise estimation of future observations. In an experimental setting this active perception system, integrated into an autonomous service robot, is evaluated in a kitchen scenario.