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The Simultaneous Localization And Mapping (SLAM) problem has been wellstudied in the robotics community, especially using mono, stereo cameras ordepth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in thelast 10 years to be very useful to perceive the environment in autonomousdriving, but few methods exist that directly use these 3D data for odometry. Wepresent a new low-drift SLAM algorithm based only on 3D LiDAR data. Our methodrelies on a scan-to-model matching framework. We first have a specific samplingstrategy based on the LiDAR scans. We then define our model as the previouslocalized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surfacerepresentation. We show experiments with the Velodyne HDL32 with only 0.40%drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 andranked among the best methods (against mono, stereo and LiDAR methods) with aglobal drift of only 0.69%.

IMLS-SLAM: scan-to-model matching based on 3D data