Globally Optimal Boresight Alignment of UAV-LiDAR Systems

• URL: http://arxiv.org/abs/2202.13501v1
• Date: Mon, 28 Feb 2022 01:48:10 GMT
• Title: Globally Optimal Boresight Alignment of UAV-LiDAR Systems
• Authors: Smitha Gopinath, Hassan L. Hijazi, Adam Collins, Julian Dann Nathan Lemons, Emily Schultz-Fellenz, Russell Bent, Amira Hijazi, Gert Riemersma
• Abstract summary: misalignments in airborne light detection and ranging (LiDAR) systems can lead to inaccurate 3D point clouds. We introduce a mixed-integer quadratically constrained program (MIQCQP) that can globally solve this misalignment problem. We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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