RGB-D object pose estimation in unstructured environments

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. A point pair feature describing both geometric shape and photometric color and its application to a voting-based 6-DOF object pose estimation.Parallel algorithms significantly accelerating the voting process on modern GPU architectures.Effective in unstructured scenes in which the prevailing segmentation-based approaches may not be applicable.