ICRA 2011 Paper Abstract


Paper TuP110.5

Men, Hao (Stevens Institute of Technology), Gebre, Biruk (Stevens Institute of Technology), Pochiraju, Kishore (Stevens Institute of Technology)

Color Point Cloud Registration with 4D ICP Algorithm

Scheduled for presentation during the Regular Sessions "Localization and Mapping III" (TuP110), Tuesday, May 10, 2011, 14:40−14:55, Room 5E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Mapping, Range Sensing, Surveillance Systems


This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP.



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