Distance statistics based method for 3D sonar point cloud image enhancement

Patent metadata

Field	Value
Publication number	US-11093790-B2
Application number	US-201716079543-A
Country	US
Kind code	B2
Filing date	Dec 8, 2017
Priority date	Mar 8, 2017
Publication date	Aug 17, 2021
Grant date	Aug 17, 2021

Abstract

The present invention discloses a distance statistics based method for 3D sonar point cloud image enhancement, comprising the following steps: (1) obtaining sonar data, and converting 3D sonar range image information corresponding to sonar data per frame into point cloud data in global coordinate; (2) using a kd-tree to search the point cloud data, and calculate Euclidean distance Lij between point Pi and the nearest K point cloud data; wherein, value range of i and j is 1≤i≤N and 1≤j≤K respectively; N refers to the total quantity of point cloud data; (3) excluding point cloud data corresponding to mean value of Lij which do not match the certain Gaussian distribution, and complete enhancement of 3D sonar point cloud image. Such method features in easy operation, high efficiency and convenience, which can effectively remove outlier points to minimize noise, and enhance point cloud image.

First claim

The invention claimed is:

1. A distance statistics based method for 3D sonar point cloud image enhancement, comprising the following steps:
(1) obtaining sonar data, and converting 3D sonar range image information corresponding to sonar data per frame into point cloud data in global coordinate;
(2) using a kd-tree to search the point cloud data, and calculating Euclidean distance Lij between point Pi and nearest K point cloud data; wherein, value range of i and j is 1≤i≤N and 1≤j≤K respectively; N refers to total quantity of the point cloud data;
(3) excluding point cloud data corresponding to mean value of Lij which do not match certain Gaussian distribution, and complete enhancement of 3D sonar point cloud image.

2. The distance statistics based method for 3D sonar point cloud image enhancement according to claim 1 , characterized in that specific procedures of said Step (2) are stated as follows:
(2-1) establishing a kd-tree for N point cloud data, and use such kd-tree to search each point Pi in the point cloud data;
(2-2) for each point Pi, using K-NN to search its K nearest point cloud data, and calculating the Euclidean distance Lij between point cloud data Pi and the K nearest point cloud data.

3. The distance statistics based method for 3D sonar point cloud image enhancement according to claim 2 , characterized in that specific procedures of the Step (3) are stated as follows:
(3-1) calculating mean value L i of K Euclidean distance L ij for point P i ;
(3-2) calculating mean value μ and standard deviation σ for N elements in L i ;
(3-3) estimating with mean value of μ and standard deviation of σ for all L i ; selecting point cloud data whose value of corresponding L i element is outside of a--b as outlier; remove the outlier to complete enhancement of 3D sonar point cloud image; wherein, a=μ−α×σ and b=μ+α×α; α is a real number, referring as expansion coefficient.

4. A distance statistics based method for 3D sonar point cloud image enhancement, comprising the following steps:
(1′) obtaining sonar data, and convert 3D sonar range image information corresponding to sonar data per frame into point cloud data in global coordinate;
(2′) using a kd-tree to search the point cloud data, and calculate Euclidean distance Lij between point Pi and all other point cloud data within its neighborhood in distance r; wherein, value range of i and j is 1≤i≤N and 1≤j≤Mi respectively;
N refers to total quantity of the point cloud data;
Mi refers to quantity of the point cloud data within neighborhood in distance r of point cloud data Pi;
(3′) excluding point cloud data corresponding to mean value of Lij which do not match certain Gaussian distribution, and complete enhancement of 3D sonar point cloud image.

5. The distance statistics based method for 3D sonar point cloud image enhancement according to claim 4 , characterized in that specific procedures of said Step (2′) are stated as follows:
(2-1′) establishing a kd-tree for N point cloud data, and use such kd-tree to search each point Pi in the point cloud data;
(2-2′) for each point Pi, searching all point cloud data within neighborhood in distance r, and calculating the Euclidean distance Lij between point cloud data Pi and all point cloud data within its neighborhood in distance r.

6. The distance statistics based method for 3D sonar point cloud image enhancement according to claim 4 , characterized in that specific procedures of the Step (3′) are stated as follows:
(3-1′) calculating mean value L i ′ of M i Euclidean distance L ij for point cloud data P i ;
(3-2′) calculating mean value μ′ and standard deviation σ′ for N elements in L i ;
(3-3′) for all L i ′, calculating means value of μ′ and standard deviation of σ′ for Gaussian distribution statistics; selecting point cloud data whose value of corresponding L i element is outside of a′--b′ as outlier; remove the outlier to complete enhancement of 3D sonar point cloud image; wherein, α′=μ′−α×σ′ and b′=μ′+α×σ′; α is a real number, referring as expansion coefficient.

Assignees

• Univ Zhejiang

Inventors

• Chen Yaowu
• Tian Xiang
• Jiang Rongxin
• Liu Xuesong

Classifications

• G06V10/758

  Involving statistics of pixels or of feature values, e.g. histogram matching · CPC title

• G06V10/763

  Non-hierarchical techniques, e.g. based on statistics of modelling distributions · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title

• G06F18/23213

  with fixed number of clusters, e.g. K-means clustering · CPC title

• G06T2207/10028

  Range image; Depth image; 3D point clouds · CPC title