  We present a robust, privacy-preserving visual localization algorithm using
event cameras. While event cameras can potentially make robust localization due
to high dynamic range and small motion blur, the sensors exhibit large domain
gaps making it difficult to directly apply conventional image-based
localization algorithms. To mitigate the gap, we propose applying
event-to-image conversion prior to localization which leads to stable
localization. In the privacy perspective, event cameras capture only a fraction
of visual information compared to normal cameras, and thus can naturally hide
sensitive visual details. To further enhance the privacy protection in our
event-based pipeline, we introduce privacy protection at two levels, namely
sensor and network level. Sensor level protection aims at hiding facial details
with lightweight filtering while network level protection targets hiding the
entire user's view in private scene applications using a novel neural network
inference pipeline. Both levels of protection involve light-weight computation
and incur only a small performance loss. We thus project our method to serve as
a building block for practical location-based services using event cameras. The
code and dataset will be made public through the following link:
https://github.com/82magnolia/event_localization.
