unnamed-1408 / eigen-based-kd-tree

A simple and efficient c++ KDTree implementation.

KD Tree

A simple and efficient c++ KD-Tree implementation. This code was written when I was learning c++11. So I deliberately wrote it all in c++ style and used some c++11 characteristics, like smart pointer and many other STL tools.

If you are interested in following features, you can try this project for fun:

• KNN Search and Radius Search are surpported and have comparable performance with PCL.
• Simple. Only 2 header-only file.
• Eigen-Based. Point cloud is organized by Eigen, and can be extended to any dimension.
• Low Dependency. All you need is Eigen.
• Smart Pointer. Not worry about memory leak when use it.

Requirements

• Eigen
• cmake 2.8+

Quick Start

mkdir build
cd build
cmake ..
make 
./main # test kdtree

Usage

After include kdtree.hpp, you can use it as following.

Eigen::MatrixXd cloud_eigen; // point cloud
cloud_eigen = ... ; // assign point cloud for cloud_eigen
int leaf_size = 5; // the minimum number of points in the leaf nodes
KDTree kdtree(cloud_eigen, leaf_size); // build kdtree
// knn search
std::vector<int> pts_idx; // indices of result points
std::vector<double> pts_dist; // distances of result points
int k = 5;
kdtree.knnSearch(point, k, pts_idx, pts_dist);
// radius search
double radius = 5;
kdtree.radiusSearch(point, radius, pts_idx, pts_dist);