A New Approach to Map Joining for Depth-Augmented Visual SLAM

Abstract

In this paper, a novel scheme is proposed to improve real-time performance of simultaneous localization and mapping (SLAM) of a mobile robot based on depth-augmented visual features. In this design, the robot has two stages in navigation applications, namely the map building stage and the map usage stage. In the map-building stage, a local map is built to join into the global map. For the map-usage stage, instead of using the global map, the local maps facilitate real-time path tracking control of the robot. Using of local maps has the merit of reducing the computational complexity of EKF-SLAM. In the map joining procedure, deviations of adjacent local maps are corrected based on local features. Loop closure detection is used to determine whether the local map building is completed. A Kinect sensor is adopted to realize the proposed method. Navigation experiments on a wheeled mobile robot show that the motion error of robot localization is within 0.1m for a travel over 83m.