应用特征估计的距离图像多尺度滤波

冯肖维; 何永义; 方明伦; 张军高

doi:10.3788/OPE.20111905.1118

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应用特征估计的距离图像多尺度滤波

信息科学 | 更新时间：2020-08-12

- 应用特征估计的距离图像多尺度滤波
- Multi-scale smoothing of noisy ranges image using feature estimation
- 光学精密工程 2011年19卷第5期页码：1118-1125
- 作者机构：
  
  上海大学 CIMS及机器人研究所上海,200072
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2007AA041604)();上海市科委重点项目(No.07DZ05805)();上海大学博士生创新基金资助项目(No.S
- DOI：10.3788/OPE.20111905.1118
  中图分类号： TP391
- 收稿日期：2010-05-31，
  
  修回日期：2010-07-05，
  
  网络出版日期：2011-05-26，
  
  纸质出版日期：2011-05-26
- 稿件说明：
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冯肖维, 何永义, 方明伦, 张军高. 应用特征估计的距离图像多尺度滤波[J]. 光学精密工程, 2011,19(5): 1118-1125

FENG Xiao-wei, HE Yong-yi, FANG Ming-lun, ZHANG Jun-gao. Multi-scale smoothing of noisy ranges image using feature estimation[J]. Editorial Office of Optics and Precision Engineering, 2011,19(5): 1118-1125
冯肖维, 何永义, 方明伦, 张军高. 应用特征估计的距离图像多尺度滤波[J]. 光学精密工程, 2011,19(5): 1118-1125 DOI： 10.3788/OPE.20111905.1118.

FENG Xiao-wei, HE Yong-yi, FANG Ming-lun, ZHANG Jun-gao. Multi-scale smoothing of noisy ranges image using feature estimation[J]. Editorial Office of Optics and Precision Engineering, 2011,19(5): 1118-1125 DOI： 10.3788/OPE.20111905.1118.

摘要

为了提取含有噪声的激光扫描距离图像中的特征

提出了一种多尺度自适应滤波方法。该方法由特征估计和多尺度滤波两部分组成。利用无嗅卡尔曼滤波器构建自适应特征估计器

估计扫描点间的几何拓扑关系

并用估计过程中所获得的Mahalanobis距离构建扩散滤波核

对原始距离像进行多尺度滤波处理。为了能够仅依靠单一模型实现对环境中不同几何元素的有效估计

介绍了一种根据距离像局部特性进行自适应调整的曲线估计模型。试验结果表明

在噪声方差为2.2510

-4

时

经自适应滤波处理后的图像的最高峰值信噪比增益达10.55 dB

均方误差减小58.24%。与基于固定模型的滤波相比

本文所述自适应模型滤波法能够使特征提取的正确率提高10%

而时间消耗减少55%。

Abstract

An adaptive smoothing algorithm within a scale space framework is proposed to extract the features of noisy range images of a laser rangefinder. The method is composed of feature estimation and multi-scale smoothing. A Unscented Kalman Filter(UKF) is used to construct an adaptive feature estimator to estimate the topology of points

then the Mahalanobis distances obtained by estimation are taken to calculate the smoothing mask. In order to provide a more efficient estimation of different major geometries by a single model

an adaptive curve model which varies depending on the local nature of range image is employed. Experimental results indicate that the Peak Signal-to-Noise Ratio (PSNR) gain of the adaptive algorithm has reached 10.55 dB and the Mean Square Error(MSE) has been reduced by 58.24% when the noise variance is 2.2510

-4

. The proposed method with an adaptive model can improve the correctness of feature extraction by 10% comparing to the smoothing algorithm with a fixed neighborhood model

while the time consuming is reduced by about 55%.

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Keywords

references

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