Detecting and Segmenting Objects for Mobile Manipulation

TitleDetecting and Segmenting Objects for Mobile Manipulation
Publication TypeConference Paper
Year of Publication2009
AuthorsRusu, Radu Bogdan., Holzbach, Andreas., Beetz, Michael., and Bradski, Gary
Conference NameICCV, S3DV Workshop
Keywordscomputer vision, manipulation, perception, robotics

This paper proposes a novel 3D scene interpretation approach for robots in
mobile manipulation scenarios using a set of 3D point features (Fast Point Feature
Histograms) and probabilistic graphical methods (Conditional Random Fields).
Our system uses real time stereo with textured light to obtain dense depth maps
in the robot's manipulators working space.  For the purposes of manipulation, we want to
interpret the planar supporting surfaces of the scene, recognize and
segment the object classes into their primitive parts in 6 degrees of freedom (6DOF) so that
the robot knows what it is attempting to use and where it may be handled.
The scene interpretation algorithm uses a two-layer classification scheme: i)~we
estimate Fast Point Feature Histograms (FPFH) as local 3D point
features to segment the objects of interest into geometric primitives; and
ii)~we learn and categorize object classes using a novel Global Fast Point Feature
Histogram (GFPFH) scheme which uses the previously estimated primitives at each
point. To show the validity of our approach, we analyze the proposed system for the
problem of recognizing the object class of 20 objects in 500 table settings
scenarios.  Our algorithm identifies the planar surfaces, decomposes the scene
and objects into geometric primitives with 98.27% accuracy and uses the
geometric primitives to identify the object's class with an accuracy of 96.69%.

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