Date of Original Version
Abstract or Description
This article compares several parameterizations and motion models for improving the estimation of the nonlinear uncertainty distribution produced by robot motion. In previous work, we have shown that the use of a modified polar parameterization provides a way to represent nonlinear measurements distributions in the Cartesian space as linear distributions in polar space. Following the same reasoning, we present a motion model extension that utilizes the same polar parameterization to achieve improved modeling of mobile robot motion in between measurements, gaining robustness with no additional overhead. We present both simulated and experimental results to validate the effectiveness of our approach.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2096-2101.