Perception

Iterative Closest Point (ICP)

Iterative Closest Point is an algorithm for registering two point clouds by alternating between matching each point to its nearest neighbor in the other cloud and computing the rigid transformation that minimizes the matched distances, iterating to convergence. Introduced by Besl and McKay in 1992, it spawned variants including point-to-plane ICP and Generalized ICP, and it requires a reasonable initial alignment to avoid local minima.

Why it matters for physical AI

ICP-style registration powers LiDAR scan matching for odometry, model-based object pose refinement for grasping, and map alignment, making it one of the most-run geometric algorithms on deployed robots.