三维距离图像基于特征估计的自适应平滑

Adaptive smoothing for three-dimensional range image based on feature estimation

为了抑制激光测距仪采集3维距离图像的噪声与畸变,提出了一种各向异性自适应平滑去噪方法。该方法集成了随机信号处理和尺度空间表述技术,根据邻域点构建特征估计模型,对距离图像中局部区域内测量点间的流形拓扑关系进行预测,并利用无嗅采样技术计算原始图像和估计模型间的马氏距离作为相似性测度构建卷积滤波核,实现三维距离图像各向异性扩散平滑去噪。通过该方法能够有效抑制原始图像发生的变形或偏移,在抑制噪声的同时突出主要特征。试验结果表明在噪声方差为4.0×10^-4 m^2时,经自适应平滑处理后的图像的峰值信噪比增益达16.41 dB,均方误差减小66.16%。本文方法能够有效提高三维距离图像的质量,为基于激光测距仪的三维环境感知与测量建模提供技术支撑。

To reduce noise and distortion of a 3D range image obtained from a laser rangefinder,an anisotropic adaptive smoothing method was introduced.The method consisted of stochastic signal estimation and scale-space representation.A feature estimation model was then derived from neighboring pointsand was used to predict the manifold topological relations between those neighboring points.To achieve anisotropic diffusion smoothing,the Mahalanobis distance between the original image and the estimated model was calculated asa similarity measure,which could then be usedtoconstruct a convolution kernel.This method enabled the distortion of the original image to be effectively corrected and noise to be suppressed.It also made the main imagefeatures more apparent.Experimental results indicate that the peak signal-to-noise ratiogain of the adaptive algorithm reached 16.41 dB,and the mean square error was reduced to 66.16%when the noise variance was 4.0×10^-4 m^2.Our smoothing method can thus improve the quality of noisy 3D range imagesand can provide technical support for 3D sensing and measurement modeling based on laser rangefinders.