Real-time Lane Marker Detection Using Template Matching with RGB-D Camera

TL;DR

This paper presents a real-time lane detection method using RGB-D camera data, combining color and depth information with template matching and geometric techniques to improve accuracy under challenging conditions.

Contribution

The approach uniquely integrates RGB-D data with template matching and geometric feature extraction for robust real-time lane detection.

Findings

Achieves 20 frames per second processing speed.

Effectively reduces noise and detects lanes in various scenarios.

Validated on synthetic and real datasets.

Abstract

This paper addresses the problem of lane detection which is fundamental for self-driving vehicles. Our approach exploits both colour and depth information recorded by a single RGB-D camera to better deal with negative factors such as lighting conditions and lane-like objects. In the approach, colour and depth images are first converted to a half-binary format and a 2D matrix of 3D points. They are then used as the inputs of template matching and geometric feature extraction processes to form a response map so that its values represent the probability of pixels being lane markers. To further improve the results, the template and lane surfaces are finally refined by principal component analysis and lane model fitting techniques. A number of experiments have been conducted on both synthetic and real datasets. The result shows that the proposed approach can effectively eliminate unwanted noise to accurately detect lane markers in various scenarios. Moreover, the processing speed of 20 frames per second under hardware configuration of a popular laptop computer allows the proposed algorithm to be implemented for real-time autonomous driving applications.