Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Object recognition and pose estimation using color cooccurrence histograms and geometric modeling
Image and Vision Computing
Probabilistic 3D object recognition based on multiple interpretations generation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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The next generation of service robots is to offer services relying heavily on visually guided manipulation, besides navigation, such services as errand, logistics, appliance, home keeping, etc. For the successful introduction of these services, it is critical to establish the consumer-level dependability in 3D object recognition and pose estimation in a natural setting where a large variation of environment, e.g., perspective, texture, form factor, illumination, occlusion, etc., is common. To address this problem, we propose an approach of the two-layered particle filter to the dependability in 3D object recognition and pose estimation. In the upper layer, a set of object pose candidates is identified and maintained in the search space as a set of super-particles, each of which is assigned a probability of the true pose and evolved in time along with the accumulation of further evidences. To define the object pose candidates, first, we acquire initially weak evidences quickly and interpret them in terms of possible object poses in space. These interpretations serve as the region of interest for detailed investigation by which the pose probabilities are computed for individual interpretations based on the likelihood and unlikelihood of various features available in the corresponding regions of interest. During the process of probability computation, we select the object pose candidates to be used as super-particles in the upper layer. In the lower level, the pose uncertainties associated with the individual candidates are represented as particles that are subject to the propagation in time. Finally, the experimental results support the strength of the proposed approach in the real environment in terms of its dependability in 3D object recognition and pose estimation.