PCL库文献翻译(一)

31
五月
2021

I.PCL Walkthrough

一.初识PCL

This tutorials will walk you through the components of your PCL installation, providing short descriptions of the modules, indicating where they are located and also listing the interaction between different components.
这个系列教程将会带领您安装PCL库组件,简略描述PCL库中的模块,告知您在哪调用模块,也将列出模块间的相互依赖关系。

在这里插入图片描述

图1.1 模块间的相互依赖(摘录于:https://www.bilibili.com/video/BV1JV411C7f3)

Ⅱ.Overview

二.总览

PCL is split in a number of modular libraries. The most important set of released PCL modules is shown below:
PCL库是一个模组化的库。PCL发行库中最重要的几个组成部分如下:
Filters滤波
Features特征提取
KeyPoints关键点
Registration

Ⅲ.Filters

三.滤波

Background
背景
An example of noise removal is presented in the figure below. Due to measurement errors, certain datasets present a large number of shadow points. This complicates the estimation of local point cloud 3D features. Some of these outliers can be filtered by performing a statistical analysis on each point’s neighborhood, and trimming those that do not meet a certain criteria. The sparse outlier removal implementation in PCL is based on the computation of the distribution of point to neighbor distances in the input dataset. For each point, the mean distance from it to all its neighbors is computed. By assuming that the resulting distribution is Gaussian with a mean and a standard deviation, all points whose mean distances are outside an interval defined by the global distances mean and standard deviation can be considered as outliers and trimmed from the dataset.
一个噪声去除案例如下所示。由于测量误差,点云中存在大量阴影点。这使得局部点云三维特征的估计变得复杂。点云模型中的部分异常值可以通过对每个点的领域进行统计分析,选中不符合特定标准的点进行剪除来滤除杂点。PCL库基于对输入数据集中点到领域距离的计算实现稀疏离群点去除算法。对于每个点,PCL都将计算它于所有领域的平均距离作为计算结果。通过假设点云计算得到的分布为具有平均值和标准差的高斯分布,所有平均距离在由全局距离平均值和标准偏差定义的区间之外的点都可以被视为离群值,并将其从数据集中进行剪除。
在这里插入图片描述
图3.1滤波示例图像
Documentation: http://docs.pointclouds.org/trunk/group__filters.html(科学上网)
Interacts with:
Sample Consensus
Kdtree
Octree
与以下交互:
随机抽样
KD树
八叉树

Ⅳ.Features

四.特征

Background
背景
A theoretical primer explaining how features work in PCL can be found in the 3D Features tutorial.
一个理论性的初级读本解释了PCL中的特性是如何工作的,可以在3D特性教程中找到。
The features library contains data structures and mechanisms for 3D feature estimation from point cloud data. 3D features are representations at certain 3D points, or positions, in space, which describe geometrical patterns based on the information available around the point. The data space selected around the query point is usually referred to as the k-neighborhood.
特征库包含了点云3D特征估计的数据结构和机制。3D特征是空间中某些表示点周围几何图案的特定三维点或位置信息。围绕查询点选择的数据空间通常称为k邻域。
The following figure shows a simple example of a selected query point, and its selected k-neighborhood.
以下图片展示了一个所选查询点及其K领域的简单案例。在这里插入图片描述
图4.1 查询点及其K领域
An example of two of the most widely used geometric point features are the underlying surface’s estimated curvature and normal at a query point p.
两个最广泛使用的几何点特征的示例是下垫面在查询点p处的估计曲率和法线。
Both of them are considered local features, as they characterize a point using the information provided by its k closest point neighbors.
两者都是局部特征,因为二者都以查询点周围的k领域为特征。
For determining these neighbors efficiently, the input dataset is usually split into smaller chunks using spatial decomposition techniques such as octrees or kD-trees, and then closest point searches are performed in that space.
为了快速定位这些领域,通常用例如八叉树、KD树等空间分解方法将输入数据集分割为小块,然后搜索此区域中的相邻点。
Depending on the application one can opt for either determining a fixed number of k points in the vicinity of p, or all points which are found inside of a sphere of radius r centered at p.
根据应用情况,可以选择在p附近确定固定数量的k点,或在以p为中心的半径r的球体内找到的所有点。
Unarguably, one the easiest methods for estimating the surface normals and curvature changes at a point p is to perform an eigendecomposition (i.e., compute the eigenvectors and eigenvalues) of the k-neighborhood point surface patch.
毫无疑问,估计查询点p的曲面法线和曲率变化的最简单方法之一是对k邻域点曲面片进行特征分解(即计算特征向量和特征值)。
Thus, the eigenvector corresponding to the smallest eigenvalue will approximate the surface normal n at point p, while the surface curvature change will be estimated from the eigenvalues as with .
因此,对应于最小特征值的特征向量将近似于p点处的曲面法线n,而曲面曲率变化将根据以下特征值进行估计:
λ0/(λ0+λ1+λ2 )其中(λ0<λ1<λ2)……………………(公式1-1)
在这里插入图片描述

图4-2 查询点及其k领域搜索示例
Documentation: http://docs.pointclouds.org/trunk/group__features.html
Interacts with:
Common
Search
KdTree
Octree
Range Image
与以下交互:
通用
搜索
KD树
八叉树
范围影像

Written By Epoch_kiwiii
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