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Proceedings Paper

Rapid and scalable 3D object recognition using LIDAR data
Author(s): Bogdan C. Matei; Yi Tan; Harpreet S. Sawhney; Rakesh Kumar
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Paper Abstract

This paper describes a model-based 3D object recognition system, which makes use of 3D data acquired by LIDAR sensors. The system is based on a coarse-to-fine scheme for object indexing and verification to achieve high efficiency and accuracy. The system employs rotationally invariant semi-local spin image features for object representation and formulates the recognition process as a feature searching through a database, followed by a matching process guided by putative candidates between the query features and the model features. To achieve recognition efficiency with sublinear dependency on the size of the model database, an approximate nearest-neighbor method, the locality-sensitivity-hashing (LSH), is used for feature search. Geometrically constrained scene-model correspondences are used to generate alignment hypotheses that are refined during a matching and verification process for achieving high recognition accuracy. A large model database of commercial and military vehicles is used for experiments. Results on real data acquired with commercial LADAR sensor systems, mounted on either high-lift or airborne platforms are presented. Our results indicate that 3D object recognition on LADAR data is matured to the point that it is ready for real large-scale applications.

Paper Details

Date Published: 18 May 2006
PDF: 11 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 623401 (18 May 2006); doi: 10.1117/12.666235
Show Author Affiliations
Bogdan C. Matei, Sarnoff Corp. (United States)
Yi Tan, Sarnoff Corp. (United States)
Harpreet S. Sawhney, Sarnoff Corp. (United States)
Rakesh Kumar, Sarnoff Corp. (United States)

Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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