Three Cameras for Robot Pose Estimation: A Triple versus Two Pairs

Mohammad E. Ragab and Kin H. Wong


Robot navigation, pose estimation, multiple cameras, extended Kalman filter


In this paper, we propose a novel layout of cameras atop a moving robot to obtain its ego-motion. In particular, we use three cameras in perpendicular setting. This layout offers a better opportunity e.g. compared to collinear settings for studying the trade-off between the accuracy of features to track and a larger field of view. We show by real experiments and synthetic data alike that using the three cameras as a triple is more advantageous when the fields of view of the cameras are slowly changing. In this case, the triple not only provide more accurate features to track but lead also to a more accurate estimation for their 3D construction. On the contrary, for pure rotations, the fields of view are rapidly changing which offers the advantage to dealing with the three cameras as two stereo pairs with a larger field of view. The extended Kalman filter (EKF) is our real-time estimator of the robot pose.

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