Weighted Range Sensor Matching Algorithms for Mobile Robot Displacement Estimation Authors

This paper introduces a “weighted” matching algorithm to estimate a robot’s planar displacement by matching dense twodimensional range scans. Based on models of expected sensor uncertainty, our algorithm weights the contribution of each scan point to the overall matching error according to its uncertainty. A general maximum likelihood formulation is used to optimally estimate the displacement between two consecutive poses. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. By explicitly modeling these noise sources, we can also calculate the actual covariance of the displacement estimates instead of a statistical approximation of it. A realistic covariance estimate is needed when further combining the displacement estimates with odometric and/or inertial measurements within a localization framework. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range finder illustrate that the method is more accurate than prior techniques.