  Predicting accurate depth with monocular images is important for low-cost
robotic applications and autonomous driving. This study proposes a
comprehensive self-supervised framework for accurate scale-aware depth
prediction on autonomous driving scenes utilizing inter-frame poses obtained
from inertial measurements. In particular, we introduce a Full-Scale depth
prediction network named FSNet. FSNet contains four important improvements over
existing self-supervised models: (1) a multichannel output representation for
stable training of depth prediction in driving scenarios, (2) an
optical-flow-based mask designed for dynamic object removal, (3) a
self-distillation training strategy to augment the training process, and (4) an
optimization-based post-processing algorithm in test time, fusing the results
from visual odometry. With this framework, robots and vehicles with only one
well-calibrated camera can collect sequences of training image frames and
camera poses, and infer accurate 3D depths of the environment without extra
labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360
dataset and the nuScenes dataset demonstrate the potential of FSNet. More
visualizations are presented in \url{https://sites.google.com/view/fsnet/home}
