Abstract: The accuracy and speed of motion determination are critical factors for vision based environment modeling of autonomous moving machines. In this paper two motion estimation algorithms for this purpose are compared using simulations. The algorithms, based on extended Kalman filtering (EKF) and Gauss-Newton minimization, determine the translations and rotations from corresponding 3-D point pairs measured from consecutive stereo images. Both solutions take the stereo measurement uncertainties into account. The simulation results show that with the same input data the accuracy of EKF is slightly lower than with the Gauss-Newton minimization approach, but its computational cost is substantially smaller. !7
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