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Compensation for Optical Axis Consistency in Visible Light-Laser Hybrid Tracking and Ranging System Based on Dual-Mirror Coupled Optical Path and Polynomial Regression Regularization

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To address the challenge of enhancing optical axis consistency in visible light-laser composite tracking and ranging systems, this paper proposes a dual-mirror coupled optical path for visible light cameras and laser rangefinders, which ensures optical axis alignment at varying distances under a fixed focal length. Furthermore, a data-driven active optical axis compensation mechanism is established: discrete zoom node data are collected, and polynomial regression modeling combined with regularization techniques is applied to mitigate optical axis jitter during the zooming process of variable-focus cameras. Experimental results demonstrate that the proposed system architecture improves overall measurement performance, and the compensation mechanism effectively corrects optical axis deviations, thereby providing technical support for the advancement of high-precision photoelectric tracking equipment.

Original languageEnglish
Title of host publicationEleventh Symposium on Novel Optoelectronic Detection Technology and Applications, NDTA 2025
EditorsPing Chen
PublisherSPIE
ISBN (Electronic)9798902324089
DOIs
StatePublished - 11 May 2026
Event11th Symposium on Novel Optoelectronic Detection Technology and Applications, NDTA 2025 - Taiyuan, China
Duration: 5 Dec 20257 Dec 2025

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume14177
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference11th Symposium on Novel Optoelectronic Detection Technology and Applications, NDTA 2025
Country/TerritoryChina
CityTaiyuan
Period5/12/257/12/25

Keywords

  • Visible light-laser composite system
  • dual-mirror coupled optical path
  • optical axis consistency
  • polynomial regression
  • regularization

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