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This paper presents an approach to probabilistic active perception planning for scene modeling in cluttered and realistic environments. When dealing with complex, multi-object scenes with arbitrary object positions, the estimation of 6D poses including their expected uncertainties is essential. The scene model keeps track of the probabilistic object hypotheses over several sequencing sensing actions...
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...
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