A Novel Radar Point Cloud Generation Method for Robot Environment Perception

Millimeter-wave (mmWave) radar has been widely used in autonomous driving due to its good performance under harsh weather conditions. In recent years, with the development of mmWave radar hardware performance, radar point clouds, as an important data format of mmWave radar, have been widely used in high-level perception tasks of mobile robots and autonomous driving. However, at present, compared to LiDAR point clouds, in common application scenes of mobile robots, mmWave radar point clouds have shortcomings such as sparsity and containing many “ghost” targets. Therefore, in this article, we analyze the reasons that cause these problems and propose a new method for point cloud generation as well as a new evaluation metric. After building a new dataset and carrying out experiments in real-world scenes, our method shows better performance on the quality of radar point clouds compared to other methods. In addition, by evaluating the performance of applying the high-quality radar point clouds to object detection tasks as well as localization and mapping tasks, the result shows that radar point clouds generated using our method can significantly improve the environment perception ability of mobile robots.

I. Introduction

In The past few years, 77-GHz millimeter-wave (mmWave) radar technology, as a low-cost alternative to LiDAR in advanced driver assistance systems [1], has been rapidly developed and has been applied to autonomous emergency braking, adaptive cruise control, and other functions. In addition to its reasonable costs, mmWave radar is robust to different lighting and weather conditions and can provide measurements of range, azimuth angles, and instantaneous velocity [2].