Park, Soonyong Kim, Soohwan Park, Mignon Park, Sung-Kee 2024-01-20T20:04:03Z 2024-01-20T20:04:03Z 2021-09-05 2009-12-15 0020-0255 https://pubs.kist.re.kr/handle/201004/131879 This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods. (C) 2009 Published by Elsevier Inc. English ELSEVIER SCIENCE INC COGNITIVE MAPS REPRESENTATION Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts Article 10.1016/j.ins.2009.06.030 1 INFORMATION SCIENCES, v.179, no.24, pp.4174 - 4198 INFORMATION SCIENCES 179 24 4174 4198 scie scopus 000271562000006 2-s2.0-70349739574 Computer Science, Information Systems Computer Science Article COGNITIVE MAPS REPRESENTATION Hybrid map Global localization Mobile robot Stereo vision Object recognition