Illumination-Invariant Localization Based on Upward Looking Scenes for Low-Cost Indoor Robots

Abstract

Abstract This paper presents a novel method for localizing a robot using an upward-looking camera under significant illumination changes. The proposed method uses two different types of visual features that are efficiently and robustly detected in upward looking indoor scenes: the orthogonal lines and local descriptor-based point features. The dominant orientation of distinguishable orthogonal lines in indoor environments is utilized to correct the robot orientation. This approach is very simple and powerful since the feature matching problem does not need to be considered when computing the dominant orientation of orthogonal lines. The local descriptors are used to effectively match local point features between two images. We present how to correct the robot location with a few noisy matches caused by a significant change of the illumination. The proposed localization method was implemented as an embedded system with an ARM11 processor on a home cleaning robot. Our experimental results show that the proposed method works well in indoor environments with various illumination changes.