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

Understand

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.