Robust mapping and localization in indoor environments

Abstract

Conventional localization methods have been developed for indoor static environments such as the home environment. In dynamic environments such as factories and warehouses, however, it is difficult to estimate the accurate robot pose. Therefore, we propose a novel approach for the estimation of the robot pose in a dynamic or large environment for which fixed features are used. In the proposed method, a ceiling-feature map is built using an upward-looking monocular camera. This map is created accurately from the robot pose using a laser scanner and an estimation based on the iterative closest point method. The ceiling-feature map consists of features such as lamps and the FREAK, and its creation can be more accurate if the sliding-window technique and bundle-adjustment schemes are used. During the post-mapping navigation, the robot pose is estimated using the Monte Carlo localization method based on the ceiling-feature map. In dynamic experiments, the proposed method shows a high repeatability and stability in real-world conditions and applications.