Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

Abstract

In vehicles for autonomous driving, light detection and ranging (LiDAR) is one of the most used sensors, along with cameras. LiDAR sensors that produce a large amount of data in one scan make it difficult to transmit and calculate data in real-time in the vehicle’s embedded system. In this paper, we propose a platform based on semantic depth data-based data reduction and reconstruction algorithms that reduce the amount of data transmission and minimize the errors between original and restored data in a vehicle system using four LiDAR sensors. The proposed platform consists of four LiDAR sensors, an integrated processing unit (IPU) that reduces the data of the LiDAR sensors, and the main processor that reconstructs the reduced data and processes the image. In the proposed platform, the 58,000 bytes of data constituting one frame detected by the VL-AS16 LiDAR sensor were reduced by an average of 87.4% to 7295 bytes by the data reduction algorithm. In the IPU placed near the LiDAR sensor, the memory usage increased by the data reduction algorithm, but the data transmission time decreased by an average of 40.3%. The transmission time where the vehicle’s processor received one frame of data decreased from an average of 1.79 to 0.28 ms. Compared with the original LiDAR sensor data, the reconstructed data showed an average error of 4.39% in the region of interest (ROI). The proposed platform increased the time required for image processing in the vehicle’s main processor by an average of 6.73% but reduced the amount of data by 87.4% with a decrease in data accuracy of 4.39%.