Polarimetric Imaging LiDAR Based on FMCW and Mueller Matrix for Target Classification

We present a three-dimensional (3D) polarimetric imaging light detection and ranging (LiDAR) based on frequency modulated continuous wave (FMCW) coherent detection and Mueller matrix polarimetry techniques for target classification. The proposed 3D polarimetric imaging LiDAR can accurately determine which point clouds (or pixels) are on target without ambiguity, which is generally challenging for conventional imaging LiDAR. To reduce artifacts caused by time-sharing polarization imaging, the spatial parallelism and beam synchronization methods are designed to simultaneously obtain multi-polarization point clouds by using Risley-prism multi-beam scanning. After acquiring the point clouds of the polarized reflection intensity, the long-range target recognition and classification can be preliminarily achieved. After calculating the Mueller matrix, the polarization characteristics of the target can be further characterized, thereby achieving more precise target recognition and classification. It is experimentally validated that the architectural materials and structural details with different polarization characteristics can be effectively distinguished and classified by using different polarization parameters, significantly improving the accuracy of target recognition. This study may open new opportunities for 3D perception of urban architecture.