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Globally Optimal Boresight Alignment of UAV-LiDAR Systems

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Smitha Gopinath, Hassan L. Hijazi, Adam Collins, Julian Dann Nathan Lemons, Emily Schultz-Fellenz, Russell Bent, Amira Hijazi, Gert Riemersma

Computer Vision and Pattern Recognition · Submitted 28 Feb 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem.

how it is stated · asserted 1.00 read the record →

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arXiv ID2202.13501 (version 1)
Pith IDKRZ45D4O, Pith's permanent record of this paper and its review
CitationAdam Collins, Amira Hijazi, Emily Schultz-Fellenz, Gert Riemersma, Hassan L. Hijazi, Julian Dann Nathan Lemons, Russell Bent, Smitha Gopinath. "Globally Optimal Boresight Alignment of UAV-LiDAR Systems." arXiv:2202.13501 (2022). https://pith.science/paper/KRZ45D4O
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,493 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.07 · mentions code or data availability 0.95.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.07 0.03 hedged possibility 0.69
s1 Determining the orientation offset, or boresight error is key to many LiDAR-based applications. background 1.00 0.05 0.04 asserted 1.00
s2 In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem. contribution 0.95 0.94 0.46 asserted 1.00
s3 We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance. contribution 0.52 0.93 0.09 asserted 1.00
s4 The nsBB relies on novel preprocessing steps that progressively reduce the problem size. method 0.97 0.88 0.05 asserted 1.00
s5 In addition, an adaptive grid search (aGS) allowing us to obtain quick heuristic solutions is presented. contribution 0.54 0.90 0.05 asserted 1.00
s6 Our algorithms are open-source, multi-threaded and multi-machine compatible. contribution 0.86 0.84 0.05 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.66.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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