Robust Pose Estimation by Fusing Partial Color and Depth Imagery

Abstract Pose estimation algorithms are an extensively studied research topic in the field of computer vision and machine learning. Even though many algorithms attempt to solve the problem, most algorithms are still not accurate enough to recover poses in real-world applications. Therefore, we have developed a new approach that utilizes depth cues and optical flow measurements that presents improved pose recovery in real-world pose estimation applications. We also present a camera calibration method that creates projection matrices for pose estimation from cameras, which enables angular comparison for relative pose estimates from two sensor systems positioned at different locations. We applied and tested the proposed algorithm in the laboratory settings and compared our findings with a commercial and a gold standard pose estimation system. Angular pose errors were reported.