A Comparison of Two Range-Based Pose Estimators for a Mobile Robot

Abstract

Pose estimation, or self-location, is a fundamental requirement for a mobile robot which enables it to navigate along a planned path or to position itself within its environment. This paper compares two 2D pose estimation algorithms: one is feature-based, and the other is iconic. Both techniques match range data to a map to calculate x, y, and (theta) (the pose) of the range sensor within its 2D environment. The metrics for the comparison include accuracy, processing time, number of range data points, robustness to errors in the map, sensitivity to the initial rough pose estimate, and environmental models. The feature-based approach is better with respect to processing time and the initial estimate; the iconic method has the advantage with respect to accuracy, the number of data points, and the environment model. For the test environment used in this paper, both methods are robust to errors in the map. A hybrid approach is proposed which combines the two estimators in a way that exploits the advantages of both.