对路边LiDAR点云角度校准的自适应方法的研究
Xin Wen1, Jiazun Hu1, Haiyu Chen1
1School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了路边光检测和测距 (LiDAR) 系统的适应性校准方法,以提高交通数据的准确性. 该技术精确地调整传感器数据,增强道路安全和智能运输研究.
科学领域:
- 机器人和自动化 机器人和自动化
- 地理空间技术是什么
- 运输工程 运输工程
背景情况:
- 光检测和测距 (LiDAR) 对于智能交通系统至关重要,提供实时交通数据.
- 路边LiDAR中的传感器错位会降低数据的准确性,阻碍道路安全和多传感器融合中的应用.
- 准确的环境感知对于先进的驾驶辅助系统和自动驾驶至关重要.
研究的目的:
- 开发和验证路边LiDAR系统的适应性校准方法.
- 为了解决 LiDAR 数据中传感器错位引起的准确性问题.
- 提高交通数据的可靠性,用于道路安全和智能交通研究.
主要方法:
- 定义了一个基于道路几何学的理想坐标系统 (X轴:向前,Y轴:路面交叉点).
- 采用卡尔曼波器 (KF) 进行轨道平滑和RANSAC进行地面安装,以便将理想的系统投射到LiDAR的坐标系统上.
- 通过比较坐标系和使用旋转矩阵对点云进行应用角度校准来计算欧勒角.
主要成果:
- 拟议的自适应校准方法使用路边LiDAR数据进行了验证.
- 实验结果表明,高精度的角度校准.
- 计算的欧勒角误差始终低于1.7%.
结论:
- 适应性校准方法有效地纠正了LiDAR传感器的错位.
- 该技术显著提高了从路边LiDAR获得的交通数据的准确性.
- 这一进步支持更可靠的道路安全监测和智能交通系统的开发.
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