Extrinsic self-calibration of an operational mobile LiDAR system

In this paper, we describe a method for automatic extrinsic self-calibration of an operational mobile LiDAR sensing system (MLS), that is additionally equipped with a POS position and orientation subsystem (e.g., GNSS/IMU, odometry). While commercial mobile mapping systems or civil LiDAR-equipped cars can be calibrated on a regular basis using a dedicated calibration setup, we aim at a method for automatic in-field (re-)calibration of such sensor systems, which is even suitable for future military combat vehicles. Part of the intended use of a mobile LiDAR or laser scanning system is 3D mapping of the terrain by POS-based direct georeferencing of the range measurements, resulting in 3D point clouds of the terrain. The basic concept of our calibration approach is to minimize the average scatter of the 3D points, assuming a certain occurrence of smooth surfaces in the scene which are scanned multiple times. The point scatter is measured by local principal component analysis (PCA). Parameters describing the sensor installation are adjusted to reach a minimal value of the PCA's average smallest eigenvalue. While sensor displacements (lever arms) are still difficult to correct in this way, our approach succeeds in eliminating misalignments of the 3D sensors (boresight alignment). The focus of this paper is on quantifying the influence of driving maneuvers and, particularly, scene characteristics on the calibration method and its results. One finding is that a curvy driving style in an urban environment provides the best conditions for the calibration of the MLS system, but other structured environments may still be acceptable.