自动驾驶汽车端到端多式传感器数据集收集框架
Junyi Gu1, Artjom Lind2, Tek Raj Chhetri3,4
1Department of Mechanical and Industrial Engineering, Tallinn University of Technology Tallinn, 12616 Tallinn, Estonia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种多功能框架,用于收集和融合来自多个传感器的数据,包括摄像头,LiDAR和雷达,以改进自动驾驶感知系统. 该框架为传感器校准,同步和数据集成提供了一个可扩展的解决方案,解决了关键的研究缺口.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 传感器技术 传感器技术
背景情况:
- 自动驾驶汽车需要使用多个冗余传感器 (如摄像头,LiDAR和雷达) 进行强大的感知.
- 传感器校准和同步对于多传感器系统至关重要,影响物体检测和路径规划.
- 现有的研究往往缺乏相机,LiDAR和雷达数据的全面融合以及可扩展的实现.
研究的目的:
- 解决多传感器融合和自动驾驶可扩展实施的研究缺口.
- 为传感器数据集收集和融合引入一个通用的,端到端的框架.
- 开发一种通用工具箱,用于校准和同步各种传感器.
主要方法:
- 开发了一个端到端的框架,集成硬件解决方案和传感器融合算法.
- 包含多种传感器:摄像头,激光雷达和雷达.
- 创建了一个基于传感器特征的传感器校准和同步的通用工具箱.
主要成果:
- 在框架原型中成功集成相机,LiDAR和雷达传感器.
- 介绍了融合算法,利用每个传感器的优势进行对象检测和跟踪.
- 证明了该框架对各种机器人和自主应用的通用性.
结论:
- 拟议的框架为多传感器感知提供了通用,可扩展和用户友好的解决方案.
- 它有效地解决了从摄像头,LiDAR和雷达中融合和同步数据的需求.
- 该框架有助于在自主系统中快速和大规模部署.
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