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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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

Qihui Guo¹, Maksim A Grigorev¹, Zihan Zhang¹

¹Department of Electric Drive, Mechatronics and Electromechanics, South Ural State University, Chelyabinsk 454080, Russia.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

We created a virtual simulation for omnidirectional vision systems, improving obstacle reconstruction and distance estimation. This platform offers a cost-effective and safe alternative to physical testbeds for robotics research.

Keywords:

computer vision environment modeling measurements omnidirectional images simulation virtual omnidirectional camera model

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Area of Science:

Robotics and Computer Vision

Background:

Omnidirectional vision is crucial for autonomous navigation and mapping.
Physical testbeds for omnidirectional vision are expensive, risky, and inefficient.

Purpose of the Study:

To develop a flexible virtual simulation platform for multi-view omnidirectional vision.
To propose and validate a novel reconstruction and ranging method using fused omnidirectional images and structured-light projection.

Main Methods:

Development of a cross-platform virtual simulation environment for omnidirectional vision.
Implementation of a fusion method combining multi-view omnidirectional images with structured-light projection.
Validation through experiments in both simulated and real-world environments.

Main Results:

Achieved high-precision obstacle contour reconstruction and distance estimation.
Demonstrated distance errors within 8 mm in real-world experiments.
Showcased robust performance across various camera configurations.

Conclusions:

The virtual simulation platform provides a practical and efficient solution for omnidirectional vision research.
The proposed fusion method enables accurate 3D perception without complex hardware or calibration.
The platform facilitates cost-effective development and testing of autonomous systems.