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 Histograms
(VFH) which classify unoccluded objects to also classifying
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 full
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
2012
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Resource Type
Text
Resource Subtype
Technical Report