In order to realise autonomous navigation of unmanned platforms in urban or off-road environments, it is crucial to study accurate, versatile and real-time road detection methods. This study proposes an adaptive road detection method that combines lane lines and obstacle boundaries, which can be applied to a variety of driving environments. Combining multi-channel threshold processing, it is robust to lane feature detection under various complex situations. Obstacle information extracted from the grid image constructed by 3D LIDAR point cloud is used for lane feature selection to avoid interference from pedestrians and vehicles. The proposed method makes use of adaptive sliding window for feature selection, and piecewise least squares method for road line fitting. Experimental results on dataset and in real-world environments show that the proposed method can overcome illumination changes, shadow occlusion, pedestrian, vehicle interference and so on in a variety of scenes. The proposed method has good enough efficiency, robustness and real-time performance.

中文翻译：

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车道线与障碍物边界相结合的自适应道路检测方法

为了在城市或越野环境中实现无人驾驶平台的自主导航，研究准确，通用和实时的道路检测方法至关重要。这项研究提出了一种自适应的道路检测方法，该方法结合了车道线和障碍物边界，可以应用于各种驾驶环境。结合多通道阈值处理，它在各种复杂情况下对车道特征检测具有鲁棒性。从3D LIDAR点云构建的栅格图像中提取的障碍物信息用于车道特征选择，以避免受到行人和车辆的干扰。该方法利用自适应滑动窗口进行特征选择，并采用分段最小二乘法进行道路线拟合。在数据集和实际环境中的实验结果表明，该方法可以克服各种场景下的光照变化，阴影遮挡，行人，车辆干扰等问题。该方法具有足够好的效率，鲁棒性和实时性。