在完全遮蔽和使用路边LiDAR的有限数据点的情况下修复车辆轨迹
Qiyang Luo1, Zhenyu Xu1, Yibin Zhang2
1Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种用于道路LiDAR车辆跟踪的新后处理方法,以克服对象封闭的挑战. 该技术重新连接车辆轨迹,并改进数据以提高准确性,提高检测范围.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 对象封闭是路边基于LiDAR的车辆跟踪的一个重大挑战.
- 堵塞导致车辆位置和速度计算的不准确性.
- 现有的方法在长时间的完全封闭和稀疏数据方面扎.
研究的目的:
- 提出一个强大的后处理方法,用于道路LiDAR车辆在遮蔽下跟踪.
- 通过解决完全封闭和有限的数据点来提高车辆跟踪的准确性.
- 提高路边LiDAR系统的有效检测范围.
主要方法:
- 介绍了一种由两部分组成的后处理方法.
- 第一部分将完全封闭导致的断开的车辆轨迹连接起来.
- 第二部分改进了车辆代表点,以提高准确性.
主要成果:
- 该方法在完全封闭时成功检测并重新连接同一辆车的轨迹.
- 更精细的车辆代表点带来了更稳定的速度估计与稀疏的数据.
- 路边LiDAR的有效检测范围显著增加.
结论:
- 拟议的方法有效地处理基于LiDAR的车辆跟踪中的物体遮蔽.
- 证明了改进的跟踪精度和扩展的检测范围.
- 这项工作支持使用路边LiDAR的排放分析和近碰撞研究中的应用.
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