Optical flow-based slip and velocity estimation technique for unmanned skid-steered vehicles

This paper proposes a novel technique to estimate slips and velocities of an unmanned skid-steered vehicle. An optical flow-based visual sensor looking down the terrain surface is employed to recover the motion of the vehicle by tracking features selected from the terrain surface. The special orientation of the on-board camera is to assure high accuracy of the motion estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematic model of the skid-steered vehicle is delicately designed to simultaneously estimate the slips and velocities. The complete non-GPS slip and velocity estimation technique is independent of terrain parameters and robust to noise and uncertainty. The SMO scheme can produce more accurate estimates than the extended Kalman filter (EKF) in the nonlinear case. Experimental results are given to show that the technique has good potential for vehicle slip and velocity estimation.