Collaborative Semantic Understanding and Mapping Framework for Autonomous Systems
نویسندگان
چکیده
Performing collaborative semantic mapping is a critical challenge for cooperative robots to enhance their comprehensive contextual understanding of the surroundings. This article bridges gap between advances in geometry that relies on pure information fusion, and single robot focuses integrating continuous raw sensor data. In this article, novel hierarchical probabilistic framework proposed, where problem formulated distributed setting. The key novelty work modeling map fusion its mathematical derivation probability decomposition. At level, point cloud obtained by combining from heterogeneous sensors used generate local maps. maps are shared among global fusion. Since voxel correspondence unknown maps, an expectation-maximization approach proposed estimate hidden data association. Then, Bayesian rule applied perform occupancy update. Experimental results unmanned aerial vehicle ground platforms show high quality demonstrating accuracy utility practical missions.
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ژورنال
عنوان ژورنال: IEEE-ASME Transactions on Mechatronics
سال: 2021
ISSN: ['1941-014X', '1083-4435']
DOI: https://doi.org/10.1109/tmech.2020.3015054