Floor segmentation of omnidirectional images for mobile robot visual navigation

Abstract

This paper describes a novel approach for purely vision based mobile robot navigation. The visual obstacle avoidance and corridor following behavior rely on the segmentation of the traversable floor region in the omnidirectional robocentric view. The image processing employs a supervised approach in which the segmentation optimal with respect to the appearance of the local environment is determined by cross validation over 3D scans captured by a photonic mixer device (PMD) camera. The range data in the front view provides the seeds and validation data to supervise the appearance based segmentation in the omniview. Segmentation relies on histogram backprojection which maintains separate appearance models for floor, obstacles and background. A naive Bayes classifier predicts the occupancy of the robots local environment by fusing the evidence provided by different segmentations and models. The classification error is analyzed on ground truth data generated by a PMD camera and manually segmented scenes. The scheme is highly robust with respect to ambiguous and misleading visual appearances of obstacles and floor, thus enabling the robot to navigate safely in unstructured environments of diverse appearance, texture and illumination. The proposed vision algorithm and the navigation behavior demonstrate a robust performance in extensive robotic experiments across several hours of autonomous operation.