SLAM Algorithm by using Global Appearance of Omnidirectional Images

摘要

This work presents a SLAM algorithm to estimate the position and orientation of a mobile robot while simultaneously creating the map of the environment. It uses only visual information provided by a catadioptric system mounted on the robot formed by a camera pointing towards a convex mirror. It provides the robot with omni-directional images that contain information with a field of view of 360 degrees around the camera-mirror axis. Each omnidirectional scene acquired by the robot is described using global appearance descriptors. Thanks to their compactness, this kind of descriptors permits running the algorithm in real time. The method consists of three different steps. First, the robot calculates the pose of the robot (location and orientation) and creates a new node in the map. This map is formed by connected nodes between them. Second, it detects loop closures between the new node and the nodes of the map. Finally, the map is optimized by using an optimization algorithm and the detected loop closures. Two different sets of images have been used to test the effectiveness of the method. They were captured in two real environments, while the robot traversed two paths. The results of the experiments show the effectiveness of our method.