Registration Algorithms

Registration algorithms: These algorithms align multiple point clouds acquired from different viewpoints or at different times to create a unified representation of the scene.

Application of the Lidar point cloud Registration Algorithms

Lidar point cloud registration algorithms are used in a variety of applications, particularly in the field of autonomous vehicles, robotics, and 3D mapping. These algorithms are used to align multiple scans of the same area, taken from different positions and at different times, into a single coherent point cloud. This allows for accurate localization and mapping of the environment, which is essential for tasks such as autonomous navigation and inspection. Lidar point cloud registration algorithms are also used in industrial applications such as quality control and monitoring of large structures, as well as in the field of archaeology for creating high-resolution 3D models of archaeological sites.

Here are the top 10 LiDAR point cloud registration algorithms, along with their download URLs and brief descriptions:

1. ICP (Iterative Closest Point) – https://github.com/ClayFlannigan/icp-registration
ICP is a classic iterative algorithm for point cloud registration. It works by iteratively aligning two point clouds by minimizing the distance between corresponding points.
2. NDT (Normal Distributions Transform) – https://github.com/ethz-asl/ndt-registration
NDT is a point cloud registration algorithm that models the point cloud as a Gaussian distribution. It estimates the pose of the target point cloud by aligning the distributions of the source and target point clouds.
3. GICP (Generalized Iterative Closest Point) – https://github.com/ClayFlannigan/gicp-registration
GICP is a variant of ICP that takes into account the covariance of the point cloud measurements. It can handle noisy and incomplete point clouds.
4. Go-ICP (Global and Outlier-aware ICP) – https://github.com/yangjiaolong/Go-ICP
Go-ICP is an extension of ICP that uses a global registration step to initialize the alignment, followed by an outlier-aware ICP step to refine the registration.
5. SC-ICP (Super Correspondence ICP) – https://github.com/xiaogangw/sc-icp
SC-ICP is an extension of ICP that uses super correspondence to establish correspondences between the two point clouds. It can handle non-rigid registration and partial overlap.
6. CPD (Coherent Point Drift) – https://github.com/gadomski/cpd-registration
CPD is a point cloud registration algorithm that models the point cloud as a probability distribution. It uses an iterative optimization algorithm to find the transformation that aligns the two point clouds.
7. PCR (Point Cloud Registration) – https://github.com/ethz-asl/pcr-registration
PCR is a point cloud registration algorithm that uses a probabilistic framework to estimate the pose of the target point cloud. It can handle partial overlap and large motion.
8. FGR (Fast Global Registration) – https://github.com/IntelVCL/FastGlobalRegistration
FGR is a fast point cloud registration algorithm that uses a feature-based approach. It extracts features from the point clouds and matches them to establish correspondences.
9. RPM (Robust Point Matching) – https://github.com/ClayFlannigan/rpm-registration
RPM is a point cloud registration algorithm that uses a probabilistic framework to estimate the pose of the target point cloud. It can handle noise, outliers, and partial overlap.
10. LORANSAC (LOw-Rank ANd Sparse matrix decomposition for RANdom SAmple Consensus) – https://github.com/ethz-asl/loransac-registration
LORANSAC is a point cloud registration algorithm that uses a low-rank and sparse matrix decomposition to estimate the transformation between two point clouds. It can handle large-scale point clouds and outliers.