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Denoising method of acoustic point cloud on the inner surface of water conveyance tunnel based on acoustic echo characteristics
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摘要: 针对基于声波反射原理的隧洞内表面损伤探测过程易受水体环境噪声、多径噪声及检测设备自身噪声干扰问题，提出一种基于声回波特性的引水隧洞内表面声点云去噪方法。该方法利用声回波固有宽度特性形成的多回波数据点及声波叠加特性形成的数据点高强度值，并结合点云空间位置信息实现团状立体隧洞内表面声点云的噪声滤波；构建系列试验以验证不同敏感参数对所提方法去噪效果的影响，并与其他经典点云滤波算法结果进行对比分析。结果表明，所提方法在隧洞内表面声点云模型的平滑、去噪及重建精度上均优于传统激光点云滤波算法。该方法可为引水隧洞内表面损伤的检测及重大水资源配置工程灾变预防提供支撑。Abstract: For the problem that the detection process of inner surface damage of tunnel based on acoustic reflection mechanism is susceptible to the interference of water environment noise, multipath noise and the noise of detection equipment, a denoising method of acoustic point cloud on the inner surface of water conveyance tunnel based on acoustic echo characteristics is proposed in this paper. The method utilizes the multi-echo data points formed by the inherent width characteristics of acoustic waves and the high-intensity values of data points formed by the superposition characteristics of acoustic waves, and combines the spatial position information of the point cloud to realize the noise filtering of the acoustic point cloud on the inner surface of the tunnel. Moreover, a series of experiments are conducted to verify the influence of different sensitive parameters on the denoising effect of the proposed method, and the results are compared with other classical point cloud filtering algorithms. The experimental results show that the proposed method is superior to the traditional laser point cloud filtering algorithm in the smoothing, denoising and reconstruction accuracy of the acoustic point cloud model on the inner surface of the tunnel. This method has practical research value for the detection of inner surface damage of water conveyance tunnel and the prevention of disasters in major water resources allocation projects.
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图 1 隧洞回波点云模型划分示意
Figure 1. Schematic diagram of area division of tunnel echo point cloud model
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图 2 特征区域数据点去噪示意
Figure 2. Schematic diagram of data point denoising in characteristic area
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图 3 不同敏感参数下误差度量值变化趋势
Figure 3. Trend of evaluation index under different sensitive parameters
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图 4 最优参数下所得点云模型图与隧洞原始点云模型图
Figure 4. The resultant point cloud model diagram and the original point cloud model diagram of the tunnel
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图 5 不同滤波算法所得结果对比
Figure 5. Comparison of results obtained by different filtering algorithms
表 1 不同滤波算法误差评价指标值
Table 1 Error metrics of different filtering algorithms
方法 Dm/cm RRMS/cm $\sigma $/cm Rh/% 半径滤波方法 5.83 6.92 3.74 5.32 高斯统计滤波方法 5.68 6.59 3.33 5.08 本文方法 4.73 5.09 1.89 2.81 
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