Accurate and robust positioning of vehicles in urban environments is of high importance for autonomous driving or mobile mapping. In mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using global navigation satellite systems are targeted. This requirement is often not guaranteed in shadowed cities where global navigation satellite system signals are usually disturbed, weak or even unavailable. We propose a novel approach which incorporates prior knowledge (i.e., a 3D city model of the environment) and improves the trajectory. The recorded point cloud is matched with the semantic city model using a point‐to‐plane iterative closest point method. A pre‐classification step enables an informed sampling of appropriate matching points. Random forest is used as classifier to discriminate between facade and remaining points. Local inconsistencies are tackled by a segmentwise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The general applicability of the method implemented is demonstrated on an inner‐city data set recorded with a mobile mapping system.

中文翻译：

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使用3D城市模型和运动学点云改善轨迹估计

车辆在城市环境中的准确和稳固的定位对于自动驾驶或移动地图至关重要。在移动制图系统中，目标是使用激光扫描同时绘制环境，并使用全球导航卫星系统进行精确定位。在阴影笼罩的城市中，通常无法保证这一要求，在这些城市中，全球导航卫星系统的信号通常会受到干扰，微弱甚至不可用。我们提出了一种新颖的方法，该方法结合了先验知识（即环境的3D城市模型）并改善了轨迹。使用点对平面迭代最近点方法将记录的点云与语义城市模型匹配。预分类步骤可以对适当的匹配点进行有信息的采样。随机森林用作分类器，以区分立面和其余点。通过点云的分段划分来解决局部不一致性，其中插值可确保分段之间的无缝过渡。该方法的一般适用性在通过移动地图系统记录的市中心数据集上得到了证明。