Scalable hybrid adjustment of images and LiDAR point clouds

Abstract

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼3000 images were matched in <1.5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment.