  Active stereo systems are used in many robotic applications that require 3D
information. These depth sensors, however, suffer from stereo artefacts and do
not provide dense depth estimates.In this work, we present the first
self-supervised depth completion method for active stereo systems that predicts
accurate dense depth maps. Our system leverages a feature-based visual inertial
SLAM system to produce motion estimates and accurate (but sparse) 3D landmarks.
The 3D landmarks are used both as model input and as supervision during
training. The motion estimates are used in our novel reconstruction loss that
relies on a combination of passive and active stereo frames, resulting in
significant improvements in textureless areas that are common in indoor
environments. Due to the nonexistence of publicly available active stereo
datasets, we release a real dataset together with additional information for a
publicly available synthetic dataset (TartanAir [42]) needed for active depth
completion and prediction. Through rigorous evaluations we show that our method
outperforms state of the art on both datasets. Additionally we show how our
method obtains more complete, and therefore safer, 3D maps when used in a
robotic platform.
