基于联合注意力机制网络的雾中激光检测
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作者单位:

1.浙江理工大学;2.浙江理工大学科艺学院;3.哈尔滨工业大学

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基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Combined attention mechanism network for laser detection in fog
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Affiliation:

1.Zhejiang Sci-Tech University;2.Keyi College of Zhejiang Sci-Tech University;3.Harbin Institute of Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    自动驾驶汽车和移动机器人均依靠激光雷达等传感器技术的快速发展而进入实际应用过程,但是激光雷达在云雾环境下测距精度和探测范围差,限制了其全天候的应用。本文根据激光在雾中的传播和后向散射模型,建立了雾中目标回波信号的模型,同时提出了一种基于卷积神经网络的联合注意力机制网络(combined attention mechanism network,CAMN),用于实现雾中目标回波信号的检测。仿真和实验结果表明,CAMN网络可以有效消除雾气对脉冲激光信号检测的干扰。在30%的散射率下,在10m范围内检测的绝对误差平均值达到3.13cm。激光雷达系统探测范围可以达到42m,是其他方法探测范围的2-3倍。该方法能有效提高雾天激光雷达测距精度和探测范围,为激光雷达的实际应用奠定基础。

    Abstract:

    With the rapid development of lidar and other sensing techniques, autonomous vehicles and mobile robotics are in the phase of real applications. But due to the poor ranging accuracy and detection range in foggy situation, the all-weather application of lidar has been limited. In this paper, the model of echo laser signals in the fog is established according to the transmission and backscattering models. A combined attention mechanism network (CAMN) based on Convolutional Neural Network (CNN) is proposed to identify the echo signal in the fog. The results of simulation and experiments show that CAMN can effectively remove the interference of fog on the detection of pulsed laser signal. The mean of absolute errors of the detection achieves 3.13cm at the range of 10m at the scattering rate of 30%. The detection range reaches 42m, doubling or tripling the numbers of other approaches. The approach can effectively improve the ranging accuracy and detection range of lidar in foggy weather. It provides the basis for real applications of lidar.

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  • 收稿日期:2023-04-12
  • 最后修改日期:2023-07-20
  • 录用日期:2023-08-08
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