ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic Object
  Removal for Static Point Cloud Mapping
Jiabao Zhang Yu Zhang
Mapping plays a crucial role in location and navigation within automatic
systems. However, the presence of dynamic objects in 3D point cloud maps
generated from scan sensors can introduce map distortion and long traces,
thereby posing challenges for accurate mapping and navigation. To address this
issue, we propose ERASOR++, an enhanced approach based on the Egocentric Ratio
of Pseudo Occupancy for effective dynamic object removal. To begin, we
introduce the Height Coding Descriptor, which combines height difference and
height layer information to encode the point cloud. Subsequently, we propose
the Height Stack Test, Ground Layer Test, and Surrounding Point Test methods to
precisely and efficiently identify the dynamic bins within point cloud bins,
thus overcoming the limitations of prior approaches. Through extensive
evaluation on open-source datasets, our approach demonstrates superior
performance in terms of precision and efficiency compared to existing methods.
Furthermore, the techniques described in our work hold promise for addressing
various challenging tasks or aspects through subsequent migration.