Globally Optimal Boresight Alignment of UAV-LiDAR Systems

TL;DR

This paper presents a globally optimal method for aligning UAV-LiDAR systems by solving a complex optimization problem, introducing efficient algorithms and heuristics to improve accuracy and computational performance.

Contribution

It introduces a novel MIQCQP formulation and a nested spatial branch and bound algorithm for precise boresight alignment of UAV-LiDAR systems, with open-source implementation.

Findings

The proposed algorithms achieve globally optimal alignment solutions.

The nested spatial branch and bound improves computational efficiency.

Adaptive grid search provides quick heuristic solutions.

Abstract

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.