A self-supervised architecture for moving obstacles classification
Román Katz, Bertrand Douillard, Juan I. Nieto, E. Nebot · 2008
This work introduces a self-supervised, multi-sensor architecture that performs automatic moving obstacles classification. Our approach presents a hierarchical scheme that relies on the ldquostabilityrdquo 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, which can be then combined to improve the final classification accuracy. The proposed architecture is general and can be instantiated in a variety of ways, using different sensors and classifiers. The applicability and validity of the proposed architecture is evaluated for a particular realization based on range and visual information that achieves 83% accuracy without using manually labeled data. Experimental results also demonstrate how accuracy can be maintained through self-training capabilities when working conditions change.