High-Definition 5MP Stereo Vision Sensing for Robotics

TL;DR

This paper presents a novel calibration and stereo matching method for 5MP stereo vision systems, enabling high-accuracy, real-time 3D sensing crucial for advanced robotic applications.

Contribution

It introduces an advanced calibration and stereo matching approach tailored for high-resolution cameras, improving accuracy and speed for robotic 3D perception.

Findings

High-resolution cameras produce better 3D point clouds with proper calibration.

The proposed method achieves real-time disparity map computation.

Calibration accuracy is critical for high-quality 3D sensing.

Abstract

High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors requires a commensurately higher level of calibration accuracy and faster processing -- requirements often unmet by conventional methods. This study addresses that critical gap by processing 5MP camera imagery using a novel, advanced frame-to-frame calibration and stereo matching methodology designed to achieve both high accuracy and speed. Furthermore, we introduce a new approach to evaluate real-time performance by comparing real-time disparity maps with ground-truth disparity maps derived from more computationally intensive stereo matching algorithms. Crucially, the research demonstrates that high-pixel-count cameras yield high-quality point clouds only through the implementation of high-accuracy calibration.