关于汽车LiDAR点云数据压缩技术的调查
Ricardo Roriz1, Heitor Silva1, Francisco Dias1
1Centro ALGORITMI/LASI, Escola de Engenharia, Universidade do Minho, 4800-058 Guimarães, Portugal.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
自动驾驶依赖于光检测和距离 (LiDAR) 传感器来进行感知. 本调查回顾了汽车LiDAR的点云压缩方法,对处理大量数据的技术进行了分类.
科学领域:
- 机器人和智能系统 机器人和智能系统
- 计算机视觉和传感器融合
- 数据压缩和信号处理.
背景情况:
- 光检测和测距 (LiDAR) 传感器对于自动驾驶至关重要,提供高分辨率的3D环境感知.
- 激光雷达的好处包括精确的测量和远程能力,即使在低光条件下.
- 来自LiDAR传感器的大数据量带来了重要的传输,处理和存储挑战.
研究的目的:
- 对汽车LiDAR点云数据进行现有数据压缩方法的调查和分类.
- 为了提供 LiDAR 压缩技术的全面分类.
- 根据关键绩效指标来比较和讨论这些方法.
主要方法:
- 对汽车LiDAR现有点云压缩技术的文献综述.
- 开发一个分类学来将压缩方法分为四个主要组.
- 对相关指标的分类方法进行比较分析.
主要成果:
- 对LiDAR数据的关键点云压缩策略的识别和分类.
- 讨论不同压缩方法的权衡和有效性.
- 强调压缩对于高效的LiDAR数据管理的重要性.
结论:
- 数据压缩对于克服自动驾驶汽车中大型LiDAR数据集所带来的挑战至关重要.
- 提出的分类学提供了当前压缩方法的结构化概述.
- 进一步研究和开发高效的LiDAR压缩对于推进自动驾驶技术至关重要.
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