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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jmjdfeig5rayzgfuauvumileza" style="color: black;">Autonomous Robots</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10514-010-9193-0">doi:10.1007/s10514-010-9193-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aw57zqdtg5gsff5ovvxqtwa7ie">fatcat:aw57zqdtg5gsff5ovvxqtwa7ie</a> </span>
more »... 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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