High-precision target positioning system for unmanned vehicles based on binocular vision

TL;DR

This paper presents a binocular vision-based system for high-precision target positioning of unmanned vehicles, achieving millimeter-level accuracy and low computation time for pose estimation of cylindrical workpieces.

Contribution

It introduces a novel high-precision positioning system combining stereo matching and RANSAC for accurate pose estimation in unmanned vehicle applications.

Findings

Position accuracy of 0.61~1.17mm

Angular accuracy of 1.95~5.13 degrees

Effective high-precision pose estimation in real experiments

Abstract

Unmanned vehicles often need to locate targets with high precision during work. In the unmanned material handling workshop, the unmanned vehicle needs to perform high-precision pose estimation of the workpiece to accurately grasp the workpiece. In this context, this paper proposes a high-precision unmanned vehicle target positioning system based on binocular vision. The system uses a region-based stereo matching algorithm to obtain a disparity map, and uses the RANSAC algorithm to extract position and posture features, which achives the estimation of the position and attitude of a six-degree-of-freedom cylindrical workpiece. In order to verify the effect of the system, this paper collects the accuracy and calculation time of the output results of the cylinder in different poses. The experimental data shows that the position accuracy of the system is 0.61~1.17mm and the angular accuracy is 1.95~5.13{\deg}, which can achieve better high-precision positioning effect.