Cached k-d tree search for ICP algorithms

The ICP (Iterative Closest Point) algorithm is the de facto standard for geometric alignment of threedimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets. 1 Background Registering 3D models is a crucial step in 3D model construction. Many application benefit from efficient ICP algorithms: Nowadays precise 3D scanners are available that are used in architecture, industrial automation, agriculture, cultural heritage conservation, and facility management. These 3D scanners deliver tons of 3D data as point clouds. Other applications of point cloud registration algorithms include medical data processing, art history,...

• Type: Conference
• Year: 2007
• Where: 3DIM
• Authors: Andreas Nüchter, Kai Lingemann, Joachim Hertzberg
• Keywords: 3DIM 2007, Closest Point, ICP Algorithms, Image Processing, K-d Trees