Title: IMLS-SLAM: scan-to-model matching based on 3D data,
Abstract: The Simultaneous Localization And Mapping (SLAM) problem has been well
studied in the robotics community, especially using mono, stereo cameras or
depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the
last 10 years to be very useful to perceive the environment in autonomous
driving, but few methods exist that directly use these 3D data for odometry. We
present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method
relies on a scan-to-model matching framework. We first have a specific sampling
strategy based on the LiDAR scans. We then define our model as the previous
localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface
representation. 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 after
4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and
ranked among the best methods (against mono, stereo and LiDAR methods) with a
global drift of only 0.69%.