Robust and Efficient Mobile Robot Self-localization using Laser Scanner and Geometrical Maps

Abstract

In this paper, we present a set of robust and efficient algorithms with O(N) cost for object detection and pose estimation with a laser ranger based on regular geometrical maps. Firstly, a multiple line fitting method is described, related with walls at the environment, which minimizes the sum of squared distances for contiguous lines and constitutes a global pattern with regular constrained angles. Secondly, beacons, related with columns at the environment, are estimated with the circle algebraic method. Two pose estimation methods are presented, based on detected beacons and lines. These methods have proved to be robust and faster than other methods. In addition to this, a redundant kinematic model is used for dead-reckoning sensors, which may improve estimations. Finally, the robot localization is performed with an EKF during normal navigation and a global localization method, based on Monte-Carlo simulation methods, at initialization.