LiDAR bore-sight calibration: a comparative study

Within the AutoProbaDTM project, we plan to develop fast and fully automated techniques to derive topographic maps from full-waveform airborne LiDAR data, based on a probabilistic approach to modelling surfaces and data acquisition, solving inverse problems and handling uncertainty. Bayesian inference provides a rigorous framework for unsupervised reconstruction of the DEM and error propagation from the data to the end result, treating all quantities as random variables. Automatic sensor calibration plays a major role in this project. In fact, the overall accuracy and uncertainty obtained from the LiDAR technology depends on the assembly and calibration of the three system components: the GPS (Global Positioning System), the INS (Inertial Navigation System) and the laser-scanner device. Bore-sight angles are the angular offsets in X,Y and Z directions between the scanner frame and the INS frame measured at the centre of the INS body frame. In this paper we evaluate some of the principal bore-sight calibration methods and we propose a novel method based on the Bayesian inference to address this problem as well. The first contribution is to use not only the 3D points extracted from the raw waveforms but their uncertainty as well, and to apply a probabilistic surface matching with spatially variable point accuracy in order to obtain the attitude corrections. The second contribution consists of using all the flight lines, where most methods only use the calibration cross. This way we can also estimate the attitude drift and correct for temporal attitude variations as well. Finally, we use the probabilistic framework for error propagation and propose a probability distribution of the calibrated bore-sight angles.