KeySLAM: Robust RGB-D Camera Tracking Using Adaptive VO and Optimal Key-Frame Selection

Kyung Min Han, Young J. Kim

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Article number9206050
Pages (from-to)6940-6947
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2020


  • Mapping
  • RGB-D Perception
  • and SLAM


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