Perception

Point Cloud Registration

Point cloud registration is the problem of estimating the rigid transformation that aligns two or more point clouds of the same scene or object. The iterative closest point algorithm of Besl and McKay (1992) refines an initial guess by alternating correspondence and transform estimation, while global methods use feature matching with robust estimation, and learned approaches regress correspondences or transforms directly.

Why it matters for physical AI

Registration underlies LiDAR odometry, map merging, and model-based object pose estimation for grasping, and its failure modes, such as poor initialization and partial overlap, drive much of the engineering in 3D perception stacks.