Robust Map Registration for Building Online Glass Confidence Maps

Abstract

Laser rangefinders (LRFs) are widely used in mobile robot localization. However, glass, which is common in indoor environments, can only be detected by LRFs in limited incident angles, instead of all incident angles like other objects. As common representations of the environments do not consider this property, glass can negatively influence the robot's localization accuracy by causing a mismatch between measurements and the map even when locations are correct. A solution to this problem is to build a glass confidence map, which shows the probability of each object in the environment to be glass. If glass confidence maps want to be built online, it is important to consider pose uncertainty. Pose uncertainty can cause incorrect registration of glass probabilities, i.e., the incorrect grid is assigned the computed glass probability. In this work, we propose a robust registration method that explicitly considers pose uncertainty. The proposed method is verified experimentally, and results show that glass confidence maps can be built online successfully and with high accuracy.