Coarse-to-fine global localization for mobile robots with hybrid maps of objects and spatial layouts

Authors:
Soonyong Park;Howon Cheong;Sung-Kee Park
Affiliations:
Center for Cognitive Robotics Research at the Korea Institute of Science and Technology, Hawolgok-dong, Sungbuk-gu, Seoul, Korea;Center for Cognitive Robotics Research at the Korea Institute of Science and Technology, Hawolgok-dong, Sungbuk-gu, Seoul, Korea;Center for Cognitive Robotics Research at the Korea Institute of Science and Technology, Hawolgok-dong, Sungbuk-gu, Seoul, Korea
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

This paper proposes a novel global localization approach that uses hybrid maps of objects and spatial layouts. We model indoor environments using the following visual cues from a stereo camera: local invariant features for object recognition and their 3D positions for object location representation. We also use a 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an object location map and a spatial layout map. Based on this modeling, we suggest a coarse-to-fine strategy for the global localization. The coarse pose is obtained by means of object recognition and point cloud fitting, and then its fine pose is estimated with a probabilistic scan matching algorithm. With real experiments, we show that our proposed method can be an effective global localization algorithm.