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Integration Of Real-Time Semantic Building Map Updating With Adaptive Monte Carlo Localization (Amcl) For Robust Indoor Mobile Robot Localization, Matthew Peavy, Pileun Kim, Hafiz Oyediran, Kyungki Kim

Durham School of Architectural Engineering and Construction: Faculty Publications

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by nonstructural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed …