《自动驾驶中的SLAM技术》对应开源代码1. 添加详细代码注释 2. 添加深蓝第一期课后习题与大作业的修改C++源码+文档说明

 # nanoflann [](https://github.com/jlblancoc/nanoflann/actions/workflows/ci-linux.yml) [](https://github.com/jlblancoc/nanoflann/actions/workflows/check-clang-format.yml) [](https://circleci.com/gh/jlblancoc/nanoflann/tree/master) [](https://ci.appveyor.com/project/jlblancoc/nanoflann/branch/master) ## 1. About *nanoflann* is a **C++11 [header-only](http://en.wikipedia.org/wiki/Header-only) library** for building KD-Trees of datasets with different topologies: R², R³ (point clouds), SO(2) and SO(3) (2D and 3D rotation groups). No support for approximate NN is provided. *nanoflann* does not require compiling or installing. You just need to `#include <nanoflann.hpp>` in your code. This library is a *fork* of the [flann library](https://github.com/flann-lib/flann) by Marius Muja and David G. Lowe, and born as a child project of [MRPT](https://www.mrpt.org/). Following the original license terms, *nanoflann* is distributed under the BSD license. Please, for bugs use the issues button or fork and open a pull request. Cite as: ``` @misc{blanco2014nanoflann, title = {nanoflann: a {C}++ header-only fork of {FLANN}, a library for Nearest Neighbor ({NN}) with KD-trees}, author = {Blanco, Jose Luis and Rai, Pranjal Kumar}, howpublished = {\url{https://github.com/jlblancoc/nanoflann}}, year = {2014} } ``` ### 1.1. Obtaining the code * Easiest way: clone this GIT repository and take the `include/nanoflann.hpp` file for use where you need it. * Debian or Ubuntu ([21.04 or newer](https://packages.ubuntu.com/source/hirsute/nanoflann)) users can install it simply with: ```bash sudo apt install libnanoflann-dev ``` * macOS users can install `nanoflann` with [Homebrew](https://brew.sh) with: ```shell $ brew install brewsci/science/nanoflann ``` or ```shell $ brew tap brewsci/science $ brew install nanoflann ``` MacPorts users can use: ``` $ sudo port install nanoflann ``` * Linux users can also install it with [Linuxbrew](https://docs.brew.sh/Homebrew-on-Linux) with: `brew install homebrew/science/nanoflann` * List of [**stable releases**](https://github.com/jlblancoc/nanoflann/releases). Check out the [CHANGELOG](https://github.com/jlblancoc/nanoflann/blob/master/CHANGELOG.md) Although nanoflann itself doesn't have to be compiled, you can build some examples and tests with: sudo apt-get install build-essential cmake libgtest-dev libeigen3-dev mkdir build && cd build && cmake .. make && make test ### 1.2. C++ API reference * Browse the [Doxygen documentation](https://jlblancoc.github.io/nanoflann/). * **Important note:** If L2 norms are used, notice that search radius and all passed and returned distances are actually *squared distances*. ### 1.3. Code examples * KD-tree look-up with `knnSearch()` and `radiusSearch()`: [pointcloud_kdd_radius.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp) * KD-tree look-up on a point cloud dataset: [pointcloud_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_example.cpp) * KD-tree look-up on a dynamic point cloud dataset: [dynamic_pointcloud_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/dynamic_pointcloud_example.cpp) * KD-tree look-up on a rotation group (SO2): [SO2_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/SO2_adaptor_example.cpp) * KD-tree look-up on a rotation group (SO3): [SO3_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/SO3_adaptor_example.cpp) * KD-tree look-up on a point cloud dataset with an external adaptor class: [pointcloud_adaptor_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_adaptor_example.cpp) * KD-tree look-up directly on an `Eigen::Matrix<>`: [matrix_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/matrix_example.cpp) * KD-tree look-up directly on `std::vector<std::vector<T> >` or `std::vector<Eigen::VectorXd>`: [vector_of_vectors_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/vector_of_vectors_example.cpp) * Example with a `Makefile` for usage through `pkg-config` (for example, after doing a "make install" or after installing from Ubuntu repositories): [example_with_pkgconfig/](https://github.com/jlblancoc/nanoflann/blob/master/examples/example_with_pkgconfig/) * Example of how to build an index and save it to disk for later usage: [saveload_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/saveload_example.cpp) * GUI examples (requires `mrpt-gui`, e.g. `sudo apt install libmrpt-gui-dev`): - [nanoflann_gui_example_R3](https://github.com/jlblancoc/nanoflann/blob/master/examples/examples_gui/nanoflann_gui_example_R3/nanoflann_gui_example_R3.cpp)  ### 1.4. Why a fork? * **Execution time efficiency**: * The power of the original `flann` library comes from the possibility of choosing between different ANN algorithms. The cost of this flexibility is the declaration of pure virtual methods which (in some circumstances) impose [run-time penalties](http://www.cs.cmu.edu/~gilpin/c%2B%2B/performance.html#virtualfunctions). In `nanoflann` all those virtual methods have been replaced by a combination of the [Curiously Recurring Template Pattern](http://en.wikipedia.org/wiki/Curiously_recurring_template_pattern) (CRTP) and inlined methods, which are much faster. * For `radiusSearch()`, there is no need to make a call to determine the number of points within the radius and then call it again to get the data. By using STL containers for the output data, containers are automatically resized. * Users can (optionally) set the problem dimensionality at compile-time via a template argument, thus allowing the compiler to fully unroll loops. * `nanoflann` allows users to provide a precomputed bounding box of the data, if available, to avoid recomputation. * Indices of data points have been converted from `int` to `size_t`, which removes a limit when handling very large data sets. * **Memory efficiency**: Instead of making a copy of the entire dataset into a custom `flann`-like matrix before building a KD-tree index, `nanoflann` allows direct access to your data via an **adaptor interface** which must be implemented in your class. Refer to the examples below or to the C++ API of [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html) for more info. ### 1.5. What can *nanoflann* do? * Building KD-trees with a single index (no randomized KD-trees, no approximate searches). * Fast, thread-safe querying for closest neighbors on KD-trees. The entry points are: * [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::knnSearch()` * Finds the `num_closest` nearest neighbors to `query_point[0:dim-1]`. Their indices are stored inside the result object. See an [example usage code](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp#L119). * [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::radiusSearch()` * Finds all the neighbors to `que