Enhancing Global Pose Estimation with Laser Range Scans using Local Techniques

摘要

Global positioning is a key issue for mobile robots. Laser range scanners are widely used on mobile robots. This paper presents new results on an algorithm which determines the pose - i.e. position and orientation - of a mobile robot by matching a 180° laser range scan to a sensor based 2D world model deduced from a set of previously recorded laser range scans. Pose estimation is done using features from a special transformation of the laser range scans, which we call gestalt features. Building up a presorted database of these gestalt features allows to achieve real-time localization. The algorithm is especially suitable for unstructured environments, because no assumption about the existence of straight, rectilinear lines, rectangular edges or similar features within the environment are made. Furthermore, the algorithm is designed to be robust when distracting temporary obstacles are present in the laser range scan or even in the world model. The rate of mislocalizations can be reduced to near zero using different kinds of local verification techniques, which are presented in this paper.