Robust Multi Sensor Pose Estimation for Medical Applications

Abstract

In this paper a sensor fusion for pose estimation using optical and inertial data is presented. The proposed algorithm is based on extended Kalman filtering and fuses data from an optical tracking system and an inertial measurement unit. These two redundant sensor systems complement each other well, with the tracking providing absolute position accuracy and the inertial measurements giving low latency information of derivatives. Models for both sensors are given respecting the different sampling times and latencies. Another key issue is to use information about every landmark, i.e. marker, visible for the tracking system, by coupling the two sensor systems tightly together. The algorithms are evaluated in simulation and tested with an experimental hardware platform. The combined sensor system provides robust pose estimation in case of short time marker occlusion and effectively compensates for latencies the pose measurements.