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

## TL;DR

This paper introduces a virtual simulation platform and a new method for high-precision mapping using omnidirectional vision and structured light.

## Contribution

A novel reconstruction method combining multi-view omnidirectional images with structured-light projection is proposed and validated.

## Key findings

- The method achieves distance estimation errors within 8 mm.
- Robust performance is demonstrated across diverse camera configurations.
- The platform reduces the need for physical calibration and hardware setups.

## 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.