Scan-to-Map Matching Using the Hausdorff Distance for Robust Mobile Robot Localization

Abstract

This paper presents a robust method for localization of mobile robots in environments that may be cluttered and that not necessarily have a polygonal structure. The estimation of the position and orientation of the robot relies on the minimization of the modified Hausdorff distance between ladar range measurements and a map of the environment. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot’s position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot in a populated environment, thus demonstrating that the approach provides a reliable and computationally feasible alternative for mobile robot localization and autonomous navigation.