Multisensor data fusion for robotic end-effector motion estimation

Abstract

In this paper, a novel robotic end-effector motion estimation approach is investigated based on multisensor data fusion to deal with the slow sampling rate and the latency of vision sensors. By using the fusion method, the missing information between two visual samples can be covered by non-vision-based sensors. When the delayed vision measurement arrives, the current estimation should be updated to cope with the error of absolute position measurement of non-vision-based sensors. The update algorithm is designed by re-calculating the prior state estimation and the innovation which both correspond to the delayed measurement. Simulation results illustrate the effectiveness of the proposed fusion approach.