  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.
