  Scan data of urban environments often include representations of dynamic
objects, such as vehicles, pedestrians, and so forth. However, when it comes to
constructing a 3D point cloud map with sequential accumulations of the scan
data, the dynamic objects often leave unwanted traces in the map. These traces
of dynamic objects act as obstacles and thus impede mobile vehicles from
achieving good localization and navigation performances. To tackle the problem,
this paper presents a novel static map building method called ERASOR,
Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal, which is
fast and robust to motion ambiguity. Our approach directs its attention to the
nature of most dynamic objects in urban environments being inevitably in
contact with the ground. Accordingly, we propose the novel concept called
pseudo occupancy to express the occupancy of unit space and then discriminate
spaces of varying occupancy. Finally, Region-wise Ground Plane Fitting (R-GPF)
is adopted to distinguish static points from dynamic points within the
candidate bins that potentially contain dynamic points. As experimentally
verified on SemanticKITTI, our proposed method yields promising performance
against state-of-the-art methods overcoming the limitations of existing ray
tracing-based and visibility-based methods.
