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.

该论文提出了一种利用单个RGB-D相机记录的颜色和深度信息来更好地处理光照条件和类车道物体等负面因素的方法，该方法将颜色和深度图像首先转换为半二进制格式和3D点的2D矩阵，然后用于模板匹配和几何特征提取处理，以形成一个响应图，其值表示像素成为车道标记的概率，最后通过主成分分析和车道模型拟合技术进一步改进模板和车道曲面。该方法在综合和真实数据集上进行了一系列实验，结果表明，该方法能够有效地消除不必要的噪声，以准确地检测各种情况下的车道标记，此外，每秒20帧的处理速度可以在流行的笔记本电脑硬件配置下进行实时自主驾驶应用。

使用RGB-D相机进行实时车道标记检测的模板匹配