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Automatic recognition of diverse 3-D objects and analysis of large urban scenes using ground and aerial LIDAR sensors

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3 Author(s)
Owechko, Y. ; HRL Labs. LLC, Malibu, CA, USA ; Medasani, S. ; Korah, Thommen

We describe a learning-based 3D object recognition pipeline developed under the DARPA URGENT program for analyzing a large LIDAR dataset collected by both airborne and ground platforms for an extended urban area. Our approach utilizes a novel strip-based cueing approach that incorporates the properties and context of urban objects. Strip-based cueing segments potential objects and assigns them to appropriate classification stages. Our learning-based recognition pipeline successfully recognized 17 3D object classes in LIDAR data collected in and over Ottawa, Canada with high efficiency and average accuracy of 70%.

Published in:

Lasers and Electro-Optics (CLEO) and Quantum Electronics and Laser Science Conference (QELS), 2010 Conference on

Date of Conference:

16-21 May 2010