(Georgia Institute of Technology, 2012)
Levihn, Martin; Dutton, Matthew; Trevor, Alexander J. B.; Stilman, Mike; Georgia Institute of Technology. Center for Robotics and Intelligent Machines
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.
(Georgia Institute of Technology, 2013-01)
Levihn, Martin; Dutton, Matthew; Trevor, Alexander J. B.; Stilman, Mike; Georgia Institute of Technology. Center for Robotics and Intelligent Machines
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.