Track-based self-supervised classification of dynamic obstacles |
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Authors: | Roman Katz Juan Nieto Eduardo Nebot Bertrand Douillard |
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Affiliation: | 1.ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics,The University of Sydney,Sydney,Australia |
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Abstract: | This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our
approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform
an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set
of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation
or to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based
on range and visual information which produces track-based labeling that is then employed to train supervised modules that
perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and
to maintain the performance through on-line retraining when working conditions change. |
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