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Semantic Depth Data Transmission Reduction Techniques using Frame-to-Frame Masking Method for Light-weighted LiDAR Signal Processing Platform

LiDAR 신호처리 플랫폼을 위한 프레임 간 마스킹 기법 기반 유효 데이터 전송량 경량화 기법

  • Received : 2021.10.09
  • Accepted : 2021.11.01
  • Published : 2021.12.31

Abstract

Multi LiDAR sensors are being mounted on autonomous vehicles, and a system to multi LiDAR sensors data is required. When sensors data is transmitted or processed to the main processor, a huge amount of data causes a load on the transport network or data processing. In order to minimize the number of load overhead into LiDAR sensor processors, only semantic data is transmitted through data comparison between frames in LiDAR data. When data from 4 LiDAR sensors are processed in a static environment without moving objects and a dynamic environment in which a person moves within sensor's field of view, in a static experiment environment, the transmitted data reduced by 89.5% from 232,104 to 26,110 bytes. In dynamic environment, it was possible to reduce the transmitted data by 88.1% to 29,179 bytes.

자율주행차량을 위해 다수의 LiDAR 센서가 차량에 탑재되고 있으며, 다수의 LiDAR 센서가 탑재됨에 따라 이를 전처리해줄 시스템이 요구되었다. 이러한 전처리 시스템을 거쳐 메인 프로세서에 센서의 데이터를 전달하거나 이를 처리할 경우 막대한 데이터양에 의해 전송 네트워크에 부하를 야기하고 이를 처리하는 메인 프로세서에도 상당한 부하를 야기하게 된다. 이러한 부하를 최소화하고자 LiDAR 센서의 데이터 중 프레임 간 데이터 비교를 통해 의미 있는 데이터만을 전송하고자 한다. 움직이는 객체가 없는 정적인 실험 환경과 센서의 시야각 내에서 사람이 움직이는 동적 실험환경에서 최대 4대의 LiDAR 센서의 데이터를 처리하였을 때, 정적 실험 환경일 경우 232,104 bytes에서 26,110 bytes로 약 89.5% 데이터 전송량을 줄일 수 있었으며, 동적 실험 환경일 경우 29,179 bytes로 약 88.1%의 데이터 전송량을 감축할 수 있었다.

Keywords

Acknowledgement

This work was supported by the Technology Innovation Program (20014592, High-Resolution 3D Solid-Stat Lidar Development) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea, 70%) and partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00944, Metamorphic approach of unstructured validation/verification for analyzing binary code, 30%).

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