Depth image reconstruction algorithm of Gm-APD LiDAR using the region growing method

The Gm-APD LiDAR can produce the three-dimensional structure of the target and possesses single-photon sensitivity, which is able to respond to extremely weak light, yet this also results that the image quality is highly susceptible to the background noise. Based on the spatio-temporal distribution characteristics of single-photon lidar data, a depth image estimation method using the region growing method is proposed. Multiple distance information is extracted from the histogram to construct a point cloud to ensure the detection rate of the echo signal. Based on the spatio-temporal distribution characteristics of point cloud data, the two-dimensional Otsu threshold method is used to denoise the point cloud, and then the region growing method is used to obtain the depth image. The sufficient simulations and experiments show that the proposed method using a small amount of data under very low signal-to-background ratio (SBR) conditions, has a better effect than the sparse Poisson intensity reconstruction algorithm (SPIRAL) when using more data. When the SBR is 0.004, the target recovery ratio of the proposed method reaches 79.3% with 0.05 s data, which is 66.4% higher than that of SPIRAL method. And when using 0.15 s data, the recovery ratio of the proposed method reaches 91.5%, which is 79.5% higher than that of SPIRAL method. The proposed method improves the suppression effect of the LiDAR system on noise, greatly improves the integrity of the target, and provides the basis for long-distance weak target detection and recognition.