Scan-to-map matching using the Hausdorff distance for robust mobile robot localization

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.