Authors:
Susana Brandão
1
;
Manuela Veloso
2
and
João P. Costeira
3
Affiliations:
1
Carnegie Mellon University and Universidade de Lisboa, United States
;
2
Carnegie Mellon University, United States
;
3
Universidade de Lisboa, Portugal
Keyword(s):
3D Partial View Representation, Robotic Vision.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Shape Representation and Matching
;
Software Engineering
Abstract:
The current paper addresses the problem of object identification from multiple 3D partial views, collected from
different view angles with the objective of disambiguating between similar objects. We assume a mobile robot
equipped with a depth sensor that autonomously grasps an object from different positions, with no previous
known pattern. The challenge is to efficiently combine the set of observations into a single classification. We
approach the problem with a sequential importance resampling filter that allows to combine the sequence of
observations and that, by its sampling nature, allows to handle the large number of possible partial views. In
this context, we introduce innovations at the level of the partial view representation and at the formulation of
the classification problem. We provide a qualitative comparison to support our representation and illustrate
the identification process with a case study.