Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets.

翻译：移动测绘，特别是移动激光扫描（MLS）技术，正日益广泛地用于以空前分辨率和精度监测与绘制城市尺度场景。由此产生的场景几何点云采样可被网格化，从而为不同应用（如可视化、模拟、导航等）创建连续表示。由于城市场景的高动态特性，长期测绘应依赖于频繁的地图更新。一种简单的方法是在每次新采集时直接用新数据替换旧数据。但这存在两个缺点：1）旧数据可能比新数据具有更高质量（分辨率、精度）；2）不同采集过程中场景覆盖范围可能不同，包括变化的遮挡情况。本文提出一种全自动化流程来解决这两个问题，通过将不同质量、覆盖范围及采集时间的网格合并问题形式化。我们的方法基于联合距离与可见性的变化检测、用于评估变化持续性的时间序列分析、基于全局布尔优化的网格拼接，以及最终通过三角形条带对所得网格碎片边界进行缝合。最后，在Robotcar和Stereopolis数据集上验证了该方法的有效性。