Gm-APD LiDAR Single-Source Data Self-Guided: Obtaining High-Resolution Depth Map

Abstract

Geiger-mode avalanche photodiode (Gm-APD) array LiDAR has become the current research focus due to its sensitive response, high precision, and easy integration. However, due to the limitations of the fabrication process and manufacturing cost, the images collected by Gm-APD array LiDAR have very serious image low-resolution problems. Since the intensity map and depth map of the target can be obtained simultaneously by relying on single-source data from Gm-APD array LiDAR, we propose a Gm-APD LiDAR single-source data self-guided method. We propose to first perform Gm-APD LiDAR intensity map superresolution, and then use the processed intensity map with the corresponding depth map for guided superresolution. The advantage of this process is that instead of using high-resolution (HR) imaging devices from different domains, it relies only on single-source data from Gm-APD LiDAR to obtain HR depth map, thus eliminating the need for additional image registration work and providing wider applicability. We investigate the feasibility of the proposed single-source data processing method and evaluate our method on the real Gm-APD LiDAR single-source data with an average peak signal-to-noise ratio of 42.21, which better preserves the original distance information of the targets while providing visually sharper outputs.

Keywords

• Geiger-mode avalanche photodiode (Gm-APD) LiDAR
• noise suppression
• self-guided method
• single-source data