Creating 3D models of the static environment is an important task for the advancement of driver assistance systems and autonomous driving. In this work, a static reference map is created from a Mobile Mapping “light detection and ranging” (LiDAR) dataset. The data was obtained in 14 measurement runs from March to October 2017 in Hannover and consists in total of about 15 billion points. The point cloud data are first segmented by region growing and then processed by a random forest classification, which divides the segments into the five static classes (“facade”, “pole”, “fence”, “traffic sign”, and “vegetation”) and three dynamic classes (“vehicle”, “bicycle”, “person”) with an overall accuracy of 94%. All static objects are entered into a voxel grid, to compare different measurement epochs directly. In the next step, the classified voxels are combined with the result of a visibility analysis. Therefore, we use a ray tracing algorithm to detect traversed voxels and differentiate between empty space and occlusion. Each voxel is classified as suitable for the static reference map or not by its object class and its occupation state during different epochs. Thereby, we avoid to eliminate static voxels which were occluded in some of the measurement runs (e.g. parts of a building occluded by a tree). However, segments that are only temporarily present and connected to static objects, such as scaffolds or awnings on buildings, are not included in the reference map. Overall, the combination of the classification with the subsequent entry of the classes into a voxel grid provides good and useful results that can be updated by including new measurement data.

创建静态环境的3D模型是推动驾驶员辅助系统和自动驾驶的重要任务。在这项工作中，从移动制图的“光检测和测距”（LiDAR）数据集创建了静态参考图。该数据是2017年3月至2017年10月在汉诺威进行的14次测量运行获得的，总计约150亿点。首先通过区域增长对点云数据进行分割，然后通过随机森林分类对其进行处理，然后将其划分为五个静态类别（“立面”，“极点”，“栅栏”，“交通标志”和“植被”）。 ）和三个动态类（“车辆”，“自行车”，“人”），总体准确度为94％。将所有静态对象输入到体素网格中，以直接比较不同的测量历元。在下一步中 分类的体素与可见性分析的结果结合在一起。因此，我们使用射线追踪算法来检测遍历的体素并区分空白空间和遮挡。根据每个对象的对象类别及其在不同时期的占用状态，将每个体素分类为适合于静态参考贴图。因此，我们避免消除在某些测量运行中被遮挡的静态体素（例如，建筑物被树遮挡的部分）。但是，仅临时存在并连接到静态对象（例如建筑物上的脚手架或遮阳篷）的线段不包括在参考地图中。总体而言，将分类与随后将类输入到体素网格中的组合提供了良好而有用的结果，可以通过添加新的测量数据来对其进行更新。