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Aligning two point clouds using Iterative Closest Point Algorithm from scratch.

Python 100.00%

point-cloud-alignment-using-icp-algorithm-scratch-implementation-'s Introduction

Point-Cloud-Alignment-using-ICP-Algorithm-scratch-implementation

Idea

Iterative Closest Point (ICP) is an alignment algorithm for merging two point clouds. It begins with an initial guess for the transformation between the point clouds. By finding corresponding points in each cloud based on proximity, ICP iteratively refines the transformation to minimize the distance between matched points. The process involves adjusting translation and rotation parameters until convergence or a set number of iterations is reached. ICP is widely applied in fields like computer vision and robotics to register and align 3D data, making it valuable for tasks such as object recognition and reconstruction.

Required Packages

  • Python
  • Numpy
  • Open3D (for visualization)

Output Visualization

  • results from open3d dataset

initial alignment o3d example final alignment o3d example



  • results from KITTI dataset

initial alignment pcd example final alignment pcd example



point-cloud-alignment-using-icp-algorithm-scratch-implementation-'s People

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