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Volume 25
Issue 20
10.3390/s25206485
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Multi-View Omnidirectional Vision and Structured Light for High-Precision Mapping and Reconstruction

by
Qihui Guo
1,
Maksim A. Grigorev
1,*,
Zihan Zhang
1,
Ivan Kholodilin
1 and
Bing Li
2
1
Department of Electric Drive, Mechatronics and Electromechanics, South Ural State University, Chelyabinsk 454080, Russia
2
Department of Automation, North China Electric Power University, Baoding 071003, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(20), 6485; https://doi.org/10.3390/s25206485 (registering DOI)
Submission received: 5 September 2025 / Revised: 10 October 2025 / Accepted: 17 October 2025 / Published: 20 October 2025
(This article belongs to the Section Navigation and Positioning)
Versions Notes

Abstract

Omnidirectional vision systems enable panoramic perception for autonomous navigation and large-scale mapping, but physical testbeds are costly, resource-intensive, and carry operational risks. We develop a virtual simulation platform for multi-view omnidirectional vision that supports flexible camera configuration and cross-platform data streaming for efficient processing. Building on this platform, we propose and validate a reconstruction and ranging method that fuses multi-view omnidirectional images with structured-light projection. The method achieves high-precision obstacle contour reconstruction and distance estimation without extensive physical calibration or rigid hardware setups. Experiments in simulation and the real world demonstrate distance errors within 8 mm and robust performance across diverse camera configurations, highlighting the practicality of the platform for omnidirectional vision research.
Keywords: virtual omnidirectional camera model; computer vision; omnidirectional images; simulation; environment modeling; measurements virtual omnidirectional camera model; computer vision; omnidirectional images; simulation; environment modeling; measurements

Share and Cite

MDPI and ACS Style

Guo, Q.; Grigorev, M.A.; Zhang, Z.; Kholodilin, I.; Li, B. Multi-View Omnidirectional Vision and Structured Light for High-Precision Mapping and Reconstruction. Sensors 2025, 25, 6485. https://doi.org/10.3390/s25206485

AMA Style

Guo Q, Grigorev MA, Zhang Z, Kholodilin I, Li B. Multi-View Omnidirectional Vision and Structured Light for High-Precision Mapping and Reconstruction. Sensors. 2025; 25(20):6485. https://doi.org/10.3390/s25206485

Chicago/Turabian Style

Guo, Qihui, Maksim A. Grigorev, Zihan Zhang, Ivan Kholodilin, and Bing Li. 2025. "Multi-View Omnidirectional Vision and Structured Light for High-Precision Mapping and Reconstruction" Sensors 25, no. 20: 6485. https://doi.org/10.3390/s25206485

APA Style

Guo, Q., Grigorev, M. A., Zhang, Z., Kholodilin, I., & Li, B. (2025). Multi-View Omnidirectional Vision and Structured Light for High-Precision Mapping and Reconstruction. Sensors, 25(20), 6485. https://doi.org/10.3390/s25206485

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