Globally Optimal Boresight Alignment of UAV-LiDAR Systems
This addresses a critical calibration issue for LiDAR-based applications in UAVs, but it is incremental as it builds on existing optimization methods for alignment problems.
The paper tackles the problem of boresight misalignment in UAV-LiDAR systems, which causes inaccurate 3D point clouds, by introducing a globally optimal MIQCQP formulation and a nested spatial branch and bound algorithm with preprocessing to improve computational performance, achieving open-source, multi-threaded solutions.
In airborne light detection and ranging (LiDAR) systems, misalignments between the LiDAR-scanner and the inertial navigation system (INS) mounted on an unmanned aerial vehicle (UAV)'s frame can lead to inaccurate 3D point clouds. Determining the orientation offset, or boresight error is key to many LiDAR-based applications. In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem. We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance. The nsBB relies on novel preprocessing steps that progressively reduce the problem size. In addition, an adaptive grid search (aGS) allowing us to obtain quick heuristic solutions is presented. Our algorithms are open-source, multi-threaded and multi-machine compatible.