  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.
