Feature extraction from ladar data using modified GPCA

Peter F. Stiller

doi:10.1117/12.910221

2 February 2012 Feature extraction from ladar data using modified GPCA

Peter F. Stiller

Proceedings Volume 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II; 829509 (2012) https://doi.org/10.1117/12.910221
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

In this paper we present a method for extracting feature information from ladar data presented in the form of a spatial point cloud. The method exploits a modified version of Generalized Principal Component Analysis (GPCA) to extract planar, or even non-linear, surface elements from this sort of data. The essential difficulty is that, depending on the aspect of the object, certain surfaces will be minimally exposed. As a result we cannot say in advance how many surfaces we are looking for, and we cannot reliably detect surfaces that are hit by only a few of the points in the cloud. An additional difficulty occurs when reconstructing the surface normal at points near where two surfaces join. The algorithm handles both issues and captures enough essential surface features to allow accurate alignment to say a CAD model for detailed recognition. One can also use the extracted planar facets as a kind of partial bounding polyhedron (modified partial bounding box) as input to an initial identification algorithm that works off of the invariants of the planar arrangement.

Citation Download Citation

Peter F. Stiller "Feature extraction from ladar data using modified GPCA", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829509 (2 February 2012); https://doi.org/10.1117/12.910221

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KEYWORDS

Data modeling

Detection and tracking algorithms

Principal component analysis

Image segmentation

Clouds

Computer aided design

LIDAR

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