A leg proprioception based 6 DOF odometry for statically stable walking robots

This article presents a 3D odometry algorithm for statically stable walking robots that only uses proprioceptive data delivered by joint angle and joint torque sensors embedded within the legs. The algorithm intrinsically handles each kind of emerging statically stable gait and is independent of predefined gait patterns. Additionally, the algorithm can be equally applied to stiff robots as well as to robots with compliant joints. Based on the proprioceptive information a 6 degrees of freedom (DOF) pose estimate is calculated in three steps. First, point clouds, represented by the foot positions with respect to the body frame at two consecutive time steps, are matched and provide a 6 DOF estimate for the relative body motion. The obtained relative motion estimates are summed up over time giving a 6 DOF pose estimate with respect to the start frame. Second, joint torque measurement based pitch and roll angle estimates are determined. Finally in a third step, these estimates are used to stabilize the orientation angles calculated in the first step by data fusion employing an error state Kalman filter. The algorithm is implemented and tested on the DLR Crawler, an actively compliant six-legged walking robot. For this specific robot, experimental data is provided and the performance is evaluated in flat terrain and on gravel, at different joint stiffness settings and for various emerging gaits. Based on this data, problems associated with the odometry of statically stable walking robots are identified and discussed. Further, some results for crossing slopes and edges in a complete 3D scenario are presented.

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