Autonomous Multiframe Point Cloud Fusion Method for mmWave Radar

This article proposed an autonomous multiframe fusion method of millimeter-wave (mmWave) radar point cloud suitable for low-crowd density indoor scenes to overcome the problem of sparse target points in the application of frequency-modulated continuous wave (FMCW) radar in indoor 4-D point cloud imaging. Without other sensors, in the static or translational state of the radar, the static and dynamic target points in the radar field of vision are distinguished through multiple velocity iterations, and then, the static target points are used to estimate the velocity of the radar itself. By calculating the displacement of the radar within a frame time, we carry out velocity filtering on the point cloud to remove the target points with large differences. Finally, the radar point cloud data of each frame is converted to the same geographic coordinate system to achieve 4-D point cloud multiframe fusion. The experimental results show that the presented method can accurately estimate the velocity of the radar and correct the coordinates of each frame point cloud. According to the imaging results, the proposed algorithm can greatly increase the imaging density of point cloud without defocusing, which improves the accuracy and readability of point cloud image with the imaging ability of static and dynamic targets.