  Learning-based visual ego-motion estimation is promising yet not ready for
navigating agile mobile robots in the real world. In this article, we propose
CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO)
designed for micro aerial vehicles (MAVs) equipped with a downward-facing
camera. The vision frontend is a content-and-uncertainty-aware homography
network (CUAHN) that is robust to non-homography image content and failure
cases of network prediction. It not only predicts the homography transformation
but also estimates its uncertainty. The training is self-supervised, so that it
does not require ground truth that is often difficult to obtain. The network
has good generalization that enables "plug-and-play" deployment in new
environments without fine-tuning. A lightweight extended Kalman filter (EKF)
serves as the VIO backend and utilizes the mean prediction and variance
estimation from the network for visual measurement updates. CUAHN-VIO is
evaluated on a high-speed public dataset and shows rivaling accuracy to
state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion
blur, low network inference time (~23ms), and stable processing latency
(~26ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded
processor to navigate a fast autonomous MAV.
