Author(s):
Roriz, Ricardo João Rei ; Cabral, Jorge ; Pinto, Sandro ; Gomes, Tiago Manuel Ribeiro
Date: 2025
Persistent ID: https://hdl.handle.net/1822/97805
Origin: RepositóriUM - Universidade do Minho
Project/scholarship:
info:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base/UIDB/00319/2020/PT;
info:eu-repo/grantAgreement/FCT//2021.06782.BD/PT;
Subject(s): Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática; Indústria, inovação e infraestruturas
Description
Light detection and ranging (LiDAR) sensors play a critical role in enabling precise and reliable environmental perception for autonomous vehicles. However, handling the large amounts of data they generate presents a significant challenge. With the emergence of standards, such as the geometry based point cloud compression (G-PCC) standard, octrees have been used as a key data structure for compressing 3-D light detection and ranging (LiDAR) data. Despite their advantages, octrees often lead to high memory utilization and computational overhead, particularly when dealing with high-resolution datasets, limiting their utilization in systems with real-time requirements. This letter presents FOG-zip: a hardware-accelerated octree compression approach designed for embedded systems with limited resources. Experimental results demonstrate that FOG-zip achieves up to a 27.8% reduction in data size compared to the same uncompressed octree bitstream, while processing each frame within the frame rate limits of the sensor used.
