  Relative localization is an important ability for multiple robots to perform
cooperative tasks in GPS-denied environment. This paper presents a novel
autonomous positioning framework for monocular relative localization of
multiple tiny flying robots. This approach does not require any groundtruth
data from external systems or manual labelling. Instead, the proposed framework
is able to label real-world images with 3D relative positions between robots
based on another onboard relative estimation technology, using ultra-wide band
(UWB). After training in this self-supervised manner, the proposed deep neural
network (DNN) can predict relative positions of peer robots by purely using a
monocular camera. This deep learning-based visual relative localization is
scalable, distributed and autonomous. We also built an open-source and
light-weight simulation pipeline by using Blender for 3D rendering, which
allows synthetic image generation of other robots, and generalized training of
the neural network. The proposed localization framework is tested on two
real-world Crazyflie2 quadrotors by running the DNN on the onboard AIdeck (a
tiny AI chip and monocular camera). All results demonstrate the effectiveness
of the self-supervised multi-robot localization method.
