  Dynamic Occupancy Grid Mapping is a technique used to generate a local map of
the environment containing both static and dynamic information. Typically,
these maps are primarily generated using lidar measurements. However, with
improvements in radar sensing, resulting in better accuracy and higher
resolution, radar is emerging as a viable alternative to lidar as the primary
sensor for mapping. In this paper, we propose a radar-centric dynamic occupancy
grid mapping algorithm with adaptations to the state computation, inverse
sensor model, and field-of-view computation tailored to the specifics of radar
measurements. We extensively evaluate our approach using real data to
demonstrate its effectiveness and establish the first benchmark for radar-based
dynamic occupancy grid mapping using the publicly available Radarscenes
dataset.
