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학술지 Multi-robot Mapping Using Omnidirectional-Vision SLAM Based on Fisheye Images
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최윤원, 권기구, 이수인, 최정원, 이석규
ETRI Journal, v.36 no.6, pp.913-923
한국전자통신연구원 (ETRI)
14ZC2400, 상황인지 스마트카를위한 다중 센서 플랫폼기술개발, 권기구
This paper proposes a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi-robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional-vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.
KSP 제안 키워드
Correction algorithm, Extraction method, Fisheye images, Fisheye lenses, Image Sensor, Long Time, Lucas-Kanade optical flow, Mapping algorithm, Motion detection, Object detection, Object extraction