Title:
Detecting Partially Occluded Objects via Segmentation and Validation
Detecting Partially Occluded Objects via Segmentation and Validation
Author(s)
Levihn, Martin
Dutton, Matthew
Trevor, Alexander J. B.
Stilman, Mike
Dutton, Matthew
Trevor, Alexander J. B.
Stilman, Mike
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Abstract
This paper presents a novel algorithm: Verfied Partial
Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds.
VPOD is implemented and validated on real sensor data
obtained by our robot. It extends Viewpoint Feature His-
tograms (VFH), which classify unoccluded objects, to also
classify partially occluded objects such as furniture that
might be seen in typical office environments. To achieve this
result, VPOD employs two strategies. First, object models
are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the complete object model is aligned back
into the scene and verified for consistency with the point
cloud data. Overall, our approach increases the number of
objects found and substantially reduces false positives due
to the verification process.
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Date Issued
2013-01
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Text
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Post-print
Proceedings
Proceedings