Real-time 3D mapping using a 2D laser scanner and IMU-aided visual SLAM

In this paper, we present a solution to 3D mapping using a 2D laser scanner with pose estimates from an IMU-aided visual SLAM system. Accurate motion estimation of a robot is achieved by visual-inertial fusion based on an extended Kalman filter (EKF). Range measurements scanned on the vertical plane are received constantly by a 2D laser scanner mounted on the robot, which are re-organized as point clouds in Cartesian space. With the pose estimates of the robot, the point clouds can be transformed into the world frame in real time. Furthermore, these point clouds received between two consecutive keyframes of the visual SLAM system are accumulated together into a unit relative to a keyframe, which can be corrected later by loop closing in visual SLAM. The 3D globally consistent map is built simultaneously by gathering these units of point clouds. The proposed approach and its performance are demonstrated and evaluated by our indoor experiments using a Turtlebot mounted with a Kinect camera, an IMU and a 2D laser scanner.