从LiDAR传感器的速度估计运动扭曲效应
Lukas Haas1,2, Arsalan Haider1,2, Ludwig Kastner1
1IFM-Institute for Driver Assistance Systems and Connected Mobility, Kempten University of Applied Sciences, Junkerstraße 1A, 87734 Benningen, Germany.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了一种新的人工神经网络方法,通过扫描光检测和射程 (LiDAR) 传感器中的运动扭曲效应来估计物体的速度和方向. 这种方法可以从单个点云中准确地估计速度,从而提高自动化车辆的感知.
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:
- 现代自动驾驶汽车依赖光检测和测距 (LiDAR) 传感器进行3D数据采集.
- 目前的LiDAR应用主要集中在使用点云进行对象检测.
- 估计物体速度通常需要对象跟踪或传感器数据融合.
研究的目的:
- 开发一种方法来估计物体的速度和方向,使用扫描LiDAR传感器固有的运动扭曲效应.
- 在不依赖传感器数据融合的情况下,从单个LiDAR点云实现速度估计.
- 利用人工神经网络来分析运动扭曲模式.
主要方法:
- 开发一个人工神经网络 (ANN) 模型.
- 使用显示运动扭曲的合成数据集对ANN的培训和评估.
- 利用运动扭曲效应作为速度估计的主要数据源.
主要成果:
- 拟议的方法准确地估计了从单个点云的物体速度和方向.
- 实现了0.1187m/s的根平均平方误差 (RMSE),用于轴向速度估计.
- 报告了0.0815m/s的RMSE,用于结果速度估计,具有狭窄的置信区间.
结论:
- 该方法从单个传感器提供可靠的4D-LiDAR数据 (包括速度).
- 能够估计自传感器独立移动的物体的速度.
- 增强自主系统中的运动预测和对象跟踪功能.
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