基于联合注意力机制网络的雾中激光检测
Combined attention mechanism network for laser detection in fog
吴龙 1朱昊伟 1杨旭 1徐璐 1陈淑玉 2张勇3
作者信息
- 1. 浙江理工大学计算机与技术学院,浙江杭州 310018
- 2. 浙江理工大学科技与艺术学院,浙江绍兴 312369
- 3. 哈尔滨工业大学光学目标仿真与测试技术研究所,黑龙江哈尔滨 150001
- 折叠
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(convolutional neural network,CNN)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10 m范围内检测的绝对误差平均值达到3.13 cm.激光雷达系统探测范围可以达到42 m,是其他方法探测范围的两三倍.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 of targets 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 3.13 cm at the range of 10 m at the scattering rate of 30％.The detection range reaches 42 m,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.
关键词
激光雷达/卷积神经网络(CNN)/光信号处理/雾中检测/联合注意力机制网络(CAMN)Key words
lidar/convolutional neural network(CNN)/optical signal processing/detection in fog/combined attention mechanism network(CAMN)引用本文复制引用
出版年
2024
NETL
NSTL
万方数据