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.
Bibliographical noteFunding Information:
This work has in part been sponsored by the Boeing Corporation (12966BC–Wing Assembly). The support is gratefully acknowledged.
© 2015 Elsevier B.V.
- Hough transform
- Pose estimation
- Range sensing
- Voting scheme