LiDAR point clouds correction acquired from a moving car based on CAN-bus data
This addresses mapping accuracy issues for autonomous vehicles or urban planning, but appears incremental as it builds on existing trajectory correction methods.
The paper tackled distortions in LiDAR point clouds caused by the slower scanning speed relative to vehicle motion, proposing a correction method using CAN-bus data and new metrics, with results indicating that accounting for vehicle trajectory improves 3D map accuracy.
In this paper, we investigate the impact of different kind of car trajectories on LiDAR scans. In fact, LiDAR scanning speeds are considerably slower than car speeds introducing distortions. We propose a method to overcome this issue as well as new metrics based on CAN bus data. Our results suggest that the vehicle trajectory should be taken into account when building 3D large-scale maps from a LiDAR mounted on a moving vehicle.