Extended Kalman Filter Based Pose Estimation Using Multiple Cameras

In this work, we solve the pose estimation problem for robot motion by placing multiple cameras on the robot. In particular, we combine the Extended Kalman Filter (EKF) with the multiple cameras. An essential strength of our approach is that it does not require finding image feature correspondences among cameras which is a difficult classical problem. The initial pose, the tracked features, and their corresponding 3D reconstruction are fed to the multiple-camera EKF which estimates the real-time pose. The reason for using multiple cameras is that the pose estimation problem is more constrained for multiple cameras than for a single camera, which has been verified by simulations and real experiments alike. Different approaches using single and two cameras have been compared, as well as two different triangulation methods for the 3D reconstruction. Both the simulations and the real experiments show that our approach is fast, robust and accurate.