This enables spatial partitioning, downsampling and search operations on the point data set. The octree library provides efficient methods for creating a hierarchical tree data structure from point cloud data. 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. 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. 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. 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. Both of them are considered local features, as they characterize a point using the information provided by its k closest point neighbors. The following figure shows a simple example of a selected query point, and its selected k-neighborhood.Īn 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. The data space selected around the query point is usually referred to as the k-neighborhood. 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 features library contains data structures and mechanisms for 3D feature estimation from point cloud data. 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.Ī theoretical primer explaining how features work in PCL can be found in the 3D Features tutorial. For each point, the mean distance from it to all its neighbors is computed. The sparse outlier removal implementation in PCL is based on the computation of the distribution of point to neighbor distances in the input dataset. 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. This complicates the estimation of local point cloud 3D features. Due to measurement errors, certain datasets present a large number of shadow points. ![]() The contents of the file will be made available to the calling script of find_package().An example of noise removal is presented in the figure below. We just need to provide the my_library-config.cmake file. Set(MY_LIBRARY_VERSION $ - matches this expression. Set(MY_LIBRARY_VERSION_MINOR 0 CACHE STRING "minor version" FORCE) Set(MY_LIBRARY_VERSION_MAJOR 1 CACHE STRING "major version" FORCE) # define library version (update: apparently you can also do it in project()!) The root CMakeLists.txt can look as follows: cmake_minimum_required(VERSION 3.0) ![]() This allows a seperation of CMake’s config files and other, unrelated configuration macros etc. The configurations will be set in the file which will be preprocessed to config_impl.hpp and included by config.hpp. The root CMakeLists.txt defines configuration options and adds the subdirectories. The library, the examples and the tool each has their own CMakeLists.txt defining the target and related code in their subdirectory. It also comes with some examples, a tool and unit tests. ![]() ![]() So we have a library consisting of various header and source files. The setupįor the scope of the tutorial, let’s say we have a library that has the following CMake structure: - include/ Throughout this post, I’ll be assuming a 3.x CMake version and an already existing CMake project. In particular, the system will be able to handle multiple installed versions and multiple configurations. In this post, I will show you how to install your library so that it can be used easily by other projects. As of version 0.5 my memory library now provides support for system-wide installation and CMake’s find_package().īecause I’ve spent hours of trial and error to come up with it, I’ll document it here.
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