LAUSR

Skeleton tracking based on multiple Kinects data fusion has been proved to have better accuracy and robustness than single Kinect. However, previous works did not consider the inconsistency of tracking accuracy in the tracking field of Kinect and the self-occlusion of human body in assembly operation, which are of vital importance to the fusion performance of the multiple Kinects data in assembly task simulation. In this work, we developed a multi-Kinect fusion algorithm to achieve robust full-body tracking in virtual reality (VR)-aided assembly simulation. Two reliability functions are first applied to evaluate the tracking confidences reflecting the impacts of the position-related error and the self-occlusion error on the tracked skeletons. Then, the tracking skeletons from multiple Kinects are fused based on weighted arithmetic average and generalized covariance intersection. To evaluate the tracking confidence, the ellipsoidal surface fitting was used to model the tracking accuracy distribution of Kinect, and the relations between the user-Kinect crossing angles and the influences of the self-occlusion on the tracking of different parts of body were studied. On the basis, the two reliability functions were developed. We implemented a prototype system leveraging six Kinects and applied the distributed computing in the system to improve the computing efficiency. Experiment results showed that the proposed algorithm has superior fusion performance compared to the peer works.