KeySLAM: Robust RGB-D Camera Tracking Using Adaptive VO and Optimal Key-Frame Selection
- 주제(키워드) and SLAM , Mapping , RGB-D Perception
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000174760
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/LRA.2020.3026964
초록/요약
We propose a novel RGB-D camera tracking system that robustly reconstructs hand-held RGB-D camera sequences. The robustness of our system is achieved by two independent features of our method: adaptive visual odometry (VO) and integer programming-based key-frame selection. Our VO method adaptively interpolates the camera motion results of the direct VO (DVO) and the iterative closed point (ICP) to yield more optimal results than existing methods such as Elastic-Fusion. Moreover, our key-frame selection method locates globally optimum key-frames using a comprehensive objective function in a deterministic manner rather than heuristic or experience-based rules that prior methods mostly rely on. As a result, our method can complete reconstruction even if the camera fails to be tracked due to discontinuous camera motions, such as kidnap events, when conventional systems need to backtrack the scene. © 2016 IEEE.
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