Affiliation:
1. Nanjing University of Science and Technology, School of Elec
2. Shanghai Geometrical Perception and Learning Co., Ltd.
Abstract
<div class="section abstract"><div class="htmlview paragraph">4D millimeter wave radar is a high-resolution sensor that has a strong perception
ability of the surrounding environment. This paper uses millimeter wave radar
point cloud to establish a static probabilistic occupancy grid map for static
environment modeling. In order to obtain a clean occupancy grid map, we classify
the point cloud according to the result of dynamic point clustering and project
the classified point cloud into the grid map. Based on the distribution and
category of millimeter wave radar point cloud, we propose a calculation model of
grid occupancy probability. After obtaining the occupancy probability according
to the calculation model, we calculate the posterior occupancy probability by
using the motion law of self-vehicle and Bayesian filtering, and construct a
stable probabilistic occupancy grid map. We test the method on real roads, and
the results show that the proposed method can effectively suppress the influence
of noise points on the quality of grid map, and improve the effect of grid map
in long-distance range.</div></div>