Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation

Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While the model is optimised for the training domain it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed via the collection of labelled data in multiple domains or by imposing priors on the type of shift between both domains. We frame the problem in the context of unsupervised domain adaptation and apply an adversarial framework to train a deep neural network with the additional objective to align features across domains. This approach benefits from adding unlabelled data and is generally applicable to many state-of-the-art architectures. Moreover, as adversarial training is notoriously hard to stabilise, we first perform an extensive ablation study on a surrogate classification task underlying the same appearance change and then apply the distilled insights to the problem of free-space segmentation for motion planning.

 

  • [PDF] M. Wulfmeier, A. Bewley, and I. Posner, “Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
    [Bibtex]

    @inproceedings{WulfmeierIROS2017,
    Author = {Wulfmeier, Markus and Bewley, Alex and Posner, Ingmar},
    Booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
    Month = {October},
    Year = {2017},
    Title = {Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation},
    Pdf = {https://arxiv.org/abs/1703.01461}
    }