Experimental validation of a visual odometry system for indoor unstructured environments

Abstract

The main goal of this paper is to present a visual odometry system, for localization of mobile robot in indoor unstructured environments, using only ceiling depth images, captured by a Kinect sensor. The use of odometric sensors is a common practice for localization of mobile robots. The method proposed in this work exploits information from an independent source of depth data and thus allows to complement or substitute the use of classic odometric sensors, like wheels encoders, with well known limitations. The experimental validation of the proposed solution shows that the method is able to accurately compute the attitude and linear velocities that allow a more precise mobile robot localization, even in presence of corrupted data from the sensor. Furthermore, the method works in an extended range of lighting conditions, without the need to perform any specific feature extraction.

Mobile Robot Localisation for Indoor Environments Based on Ceiling Pattern Recognition

“…Rodrigues at al. made studies in warehouse environments using visual odometry of ceiling images [6], Markov Localization [7] and a 3D camera applied to a depth map of the ceiling. Carreira et al [8] also use depth maps and introduce a technique to deal with missing data from the 3D camera.…”