Robustness Certification of Visual Perception Models via Camera Motion Smoothing
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
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DOI：
10.48550/arxiv.2210.04625
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Hanjiang Hu;Zuxin Liu;Linyi Li;Jiacheng Zhu;Ding Zhao
中科院分区：
文献类型：
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作者：
Hanjiang Hu;Zuxin Liu;Linyi Li;Jiacheng Zhu;Ding Zhao
大量文献表明，基于学习的视觉感知模型对敌对噪声敏感，但很少有研究考虑机器人感知模型在广泛存在的摄像机运动扰动下的鲁棒性。为此，我们研究了摄像机运动扰动下视觉感知模型的鲁棒性，以研究摄像机运动对机器人感知的影响。具体来说，我们提出了一种针对任意图像分类模型的运动平滑技术，该技术在摄像机运动扰动下的鲁棒性得到了验证。提出的基于摄像机运动平滑的鲁棒性认证框架为视觉感知模块提供了紧密和可扩展的鲁棒性保证，使其适用于广泛的机器人应用。据我们所知，这是第一次为深度感知模块提供针对相机运动的鲁棒性认证，这提高了机器人感知的可信度。一个真实的室内机器人数据集，具有整个房间的密集点云图，MetaRoom，用于具有挑战性的可认证鲁棒感知任务。我们进行了大量的实验来验证通过运动平滑对相机运动扰动的认证方法。我们的框架在-0.1m ~ 0.1m的深度方向上保证了81.7%的摄像机平移摄动认证精度。我们还通过在机器人手臂上使用眼手相机进行硬件实验，验证了我们的方法在现实世界机器人上的有效性。代码可在https://github.com/HanjiangHu/camera-motion-smoothing上获得。
A vast literature shows that the learning-based visual perception model is sensitive to adversarial noises, but few works consider the robustness of robotic perception models under widely-existing camera motion perturbations. To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception. Specifically, we propose a motion smoothing technique for arbitrary image classification models, whose robustness under camera motion perturbations could be certified. The proposed robustness certification framework based on camera motion smoothing provides tight and scalable robustness guarantees for visual perception modules so that they are applicable to wide robotic applications. As far as we are aware, this is the first work to provide robustness certification for the deep perception module against camera motions, which improves the trustworthiness of robotic perception. A realistic indoor robotic dataset with a dense point cloud map for the entire room, MetaRoom, is introduced for the challenging certifiable robust perception task. We conduct extensive experiments to validate the certification approach via motion smoothing against camera motion perturbations. Our framework guarantees the certified accuracy of 81.7% against camera translation perturbation along depth direction within -0.1m ~ 0.1m. We also validate the effectiveness of our method on the real-world robot by conducting hardware experiments on the robotic arm with an eye-in-hand camera. The code is available at https://github.com/HanjiangHu/camera-motion-smoothing.