RGB-D Object Pose Estimation

  • Changhyun Choi
  • Henrik I. Christensen
    We present an object pose estimation approach exploiting both geometric depth and photometric color information available from an RGB-D sensor. In contrast to various efforts relying on object segmentation with a known background structure, our approach does not depend on the segmentation and thus exhibits superior performance in unstructured environments. Inspired by a voting-based approach employing an oriented point pair feature, we present a voting-based approach which further incorporates color information from the RGB-D sensor and which exploits parallel power of the modern parallel computing architecture. The proposed approach is extensively evaluated with three state-of-the-art approaches on both synthetic and real datasets, and our approach outperforms the other approaches in terms of both computation time and accuracy.


    Changhyun Choi, Henrik I. Christensen, “RGB-D Object Pose Estimation in Unstructured Environments,” Robotics and Autonomous Systems, Jan. 2016. [ pdf ]

    Changhyun Choi, Henrik I. Christensen, “3D Pose Estimation of Daily Objects Using an RGB-D Camera,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vila Moura, Algarve, Portugal, 2012. [ pdf ]