  In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs),
fast maneuvers stay challenging mainly because of the big visual disparity and
motion blur. In the pursuit of higher robustness, we study convolutional neural
networks (CNNs) that predict the relative pose between subsequent images from a
fast-moving monocular camera facing a planar scene. Aided by the Inertial
Measurement Unit (IMU), we mainly focus on translational motion. The networks
we study have similar small model sizes (around 1.35MB) and high inference
speeds (around 10 milliseconds on a mobile GPU). Images for training and
testing have realistic motion blur. Departing from a network framework that
iteratively warps the first image to match the second with cascaded network
blocks, we study different network architectures and training strategies.
Simulated datasets and a self-collected MAV flight dataset are used for
evaluation. The proposed setup shows better accuracy over existing networks and
traditional feature-point-based methods during fast maneuvers. Moreover,
self-supervised learning outperforms supervised learning. Videos and
open-sourced code are available at https://github.com/tudelft/PoseNet_Planar
