  Depth perception is crucial for a wide range of robotic applications.
Multi-frame self-supervised depth estimation methods have gained research
interest due to their ability to leverage large-scale, unlabeled real-world
data. However, the self-supervised methods often rely on the assumption of a
static scene and their performance tends to degrade in dynamic environments. To
address this issue, we present Motion-Aware Loss, which leverages the temporal
relation among consecutive input frames and a novel distillation scheme between
the teacher and student networks in the multi-frame self-supervised depth
estimation methods. Specifically, we associate the spatial locations of moving
objects with the temporal order of input frames to eliminate errors induced by
object motion. Meanwhile, we enhance the original distillation scheme in
multi-frame methods to better exploit the knowledge from a teacher network. MAL
is a novel, plug-and-play module designed for seamless integration into
multi-frame self-supervised monocular depth estimation methods. Adding MAL into
previous state-of-the-art methods leads to a reduction in depth estimation
errors by up to 4.2% and 10.8% on KITTI and CityScapes benchmarks,
respectively.
