An online implementation of robust RGB-D SLAM

This paper presents an online robust RGB-D SLAM algorithm which uses an improved switchable constraints robust pose graph slam alongside with radial variance based hash function as the loop detector. The switchable constraints robust back-end is improved by initialization of its weights according to information matrix of the loops and is validated using real world datasets. The radial variance based hash function is combined with an online image to map comparison to improve accuracy of loop detection. The whole algorithm is implemented on K. N. Toosi University mobile robot with a Microsoft Kinect camera as the RGB-D sensor and the whole algorithm is validated using this robot, while the map of the environment is generated in an online fashion.