车载激光点云道路边界提取的Snake方法

针对车载激光点云中道路边界提取困难，自动化程度低的问题，提出一种基于离散点Snake的车载激光点云道路边界提取方法。不同于传统基于图像建立Snake，本文直接基于离散点建立Snake模型。先利用伪轨迹点数据，确定初始轮廓位置，参数化不同类型的道路边界初始轮廓；然后基于离散点构建适合多类型道路边界的Snake模型，定义模型内部、外部和约束能量，通过能量函数最小化推动轮廓曲线移动到显著道路边界特征点处，实现不同道路边界的精细提取。本文试验采用3份不同城市场景的车载激光点云数据验证本文方法的有效性，道路边界提取结果的准确率达到97.62%，召回率达到98.04%，F1-Measure值达到97.83%以上，且提取的道路边界结果与软件交互提取的结果有较好的吻合度。试验结果表明，本文方法能够修正噪声、断裂等数据质量对道路边界提取的影响，能够实现各类复杂城市环境中不同形状道路边界的提取，具有较强的稳健性和适用性。

Road boundaries extraction from mobile laser scanning point clouds based on discrete point Snake

Due to large volume of data, uneven spatial distribution of point densities, and complex scene, it is difficult to automatically extract and delineate road boundaries from mobile laser scanning (MLS) point clouds. This paper proposed a method for extracting road boundaries from MLS point clouds based on Snake. Different from the traditional Snake model defined within 2D image, we modified the Snake model directly based on 3D point clouds. Firstly, we developed a way of initializing Snake curves using pseudo-trajectory data to match multi-type road boundaries. Then, the Snake’s energy function was designed with internal, external and constrained energy terms derived from Snake curve points with their neighborhood points. Finally, the road boundaries were precisely extracted by minimizing the Snake’s energy function. Experiments were undertaken to evaluate the validities of the proposed algorithm with three different urban scene datasets acquired by different MLS system. Quantitative evaluations on the selected MLS datasets indicate that the Precision, Recall and F₁-Measure of road boundaries extraction results were over 97.62%, 98.04%, 97.83%, respectively, which validate that the proposed method proved a promising and robust solution for regular and irregular road boundaries extraction in complex urban environments.