Particle-filter-based Pose Estimation from Controlled Motion with Application to Visual Servoing

Abstract

In this paper, we present a Bayesian algorithm based on particle filters to estimate the camera pose for vision-based control. The state model is represented as a relative camera pose between the current and initial camera frames. The particles in the prior motion model are drawn using the velocity control signal collected from the visual controller of the robot. The pose samples are evaluated using an epipolar geometry measurement model and a suitable weight is associated with each sample. The algorithm takes advantage of the a priori knowledge about motion, i.e., the velocity computed by the visual servo control, to estimate the magnitude of the translation in addition to its direction, hence producing a full camera motion estimate. Its application to position-based visual servoing is demonstrated. Experiments are carried out using a real robot setup. The results show the efficiency of the proposed filter over the motion measurements of the robot. In addition, the filter was able to recover the split performed by the robot joints.