Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
Learning hierarchical object maps of non-stationary environments with mobile robots
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Hierarchical object discovery and dense modelling from motion cues in RGB-D video
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Moving objects are present in many robotic applications. An accurate detection and motion estimation of these objects can be crucial for the success and safety of the robot and people surrounding it. This paper presents a new probabilistic framework for clustering dependent or relational data, applied to the problem of motion clustering and estimation. While conventional techniques such as scan differencing perform well in many cases, they usually assume that a good pose estimate is available and fail when points belonging to dynamic objects show some overlap in consecutive readings. The technique proposed, CRF-Clustering, by explicitly reasoning about the underlying motion of the object, is able to deal with poor initial motion estimate and overlapping points. Moreover, it is able to consider the dependencies between neighbor points in the scans to reduce the noise in the clustering assignment. The model parameters can be estimated from labeled data in a statistically sound learning procedure. Experiments show that CRF-Clustering is able to detect moving objects, cluster them and estimate their motion.