Simultaneous Localization and Mapping with People
نویسندگان
چکیده
This paper describes a novel approach for focusing the search for victims of a rescue robot in an unknown environment. Such a rescue operation under time constraints is driven by three opposing factors, namely the need to explore unknown territory, the requirement to maintain robot localization performance as well as map quality in the face of uncertainty, and the need to target the search to areas where victims are likely to be found. Key to our approach is to leverage knowledge from victims on the ground to focus and direct the search process. We assume that victims possess useful knowledge about where potentially other victims reside and integrate these directions with a principled exploration mechanism for particle filter-based simultaneous localization and mapping (SLAM). This paper includes a set of proposed evaluations in simulation and on a real robot that aim to validate whether gestural directions can optimize the search for victims when operating under time constraints. We see this work as a first step towards enabling future applications in search and rescue where humans and robots collaborate in the same physical space to search for victims.
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