Calculate the Hamming distance between two binary features

Get Bound of a Point Cloud

* \brief Principle Component Analysis (PCA) of the Point Cloud with fixed search radius * \param[in] inputPointCloud is the input Point Cloud (XYZI

main entrance

KM main entrances*/

pcl::PointCloud<pcl::PointXYZI>::Ptr Registration::output(const pcXYZIPtr &cloud, Eigen::Matrix4d &Rt)

Constructor*/

* \brief Estimate the normals of the input Point Cloud by PCL speeding up with OpenMP * \param[in] inputPointCloud is the input Point Cloud Pointer

* \brief Use PCL to accomplish the Principle Component Analysis (PCA) * of one point and its neighborhood * \param[in] inputPointCloud is the in

* \brief Check the Normals (if they are finite) * \param normals is the input Point Cloud (XYZI)'s Normal Pointer */

Constructor

Filter the point cloud according to the horizontal and vertical distance to the lidar center

Get Bound of Subsets of a Point Cloud

Constructor: Input Graph, KM_eps threshold and prior mismatch threshold*/

1

3

2

Comparison with other methods

SOR (Statisics Outliers Remover);

Constructor

How many 1 in a binary number converted from a Hexadecimal

For target point cloud, only 1 feature vector (coordinate system) for 1 keypoint For source point cloud, 4 [6DOF case], 2 [4DOF case] feature vector (

Do 2D PCA

Do 3D PCA (with the assist of OpenCV)

Do weighted 3D PCA

Binarize the feature

Calculate the feature of 3 projection planes according to the voxel

Calculate the feature of 3 projection planes according to the voxel and the pairwise comparison feature

Calculate the feature of 3 projection planes according to the voxel and the pairwise comparison feature (compare with each projection plane)

Construct local coordinate system (LCS) for each key point (take the primary direction as the Z axis and the second primary direction as the X axis

Generate voxel according to point density*/

entrance for each keypoint 4DOF -> 6DOF