An efficient SLAM system only using RGBD sensors

This paper describes an efficient SLAM system only using RGBD sensors. This system utilizes the Microsoft Kinects to provide visual odometry estimation and 2D range scans. The Kinect looking up toward the ceiling can track the robot's trajectory through visual odometry method, which can provide more accurate motion estimation compared to wheel motion measurement and cannot be disturbed under wheel slippage. This is because the Kinect can provide a color image as well as depth information such that robust 3D feature points matching using invariant 2D feature descriptors such as SURF and FAST is possible. Furthermore, the straight line features on the ceiling can provide additional constraints on the inter-frame motion of the camera and the loop closure leading to a more accurate pose estimate. While the other two contiguous horizontal Kinects can provide wide range scans, which ensure more robust scan matching in the RBPF-SLAM framework. In addition, we develop a novel proposal distribution that relies on visual odometry by replacing the transition motion model to towards a SLAM solution. Subsequently, the accurate grid map is online learnt through the adaptive resample Rao-Blackwellized particle filter. Finally, our experimental results, using three Kinects carried on mobile platform of TurtleBot, clearly show the performance of our method.