  Safe and efficient path planning is crucial for autonomous mobile robots. A
prerequisite for path planning is to have a comprehensive understanding of the
3D structure of the robot's environment. On MAVs this is commonly achieved
using low-cost sensors, such as stereo or RGB-D cameras. These sensors may fail
to provide depth measurements in textureless or IR-absorbing areas and have
limited effective range. In path planning, this results in inefficient
trajectories or failure to recognize a feasible path to the goal, hence
significantly impairing the robot's mobility. Recent advances in deep learning
enables us to exploit prior experience about the shape of the world and hence
to infer complete depth maps from color images and additional sparse depth
measurements. In this work, we present an augmented planning system and
investigate the effects of employing state-of-the-art depth completion
techniques, specifically trained to augment sparse depth maps originating from
RGB-D sensors, semi-dense methods and stereo matchers. We extensively evaluate
our approach in online path planning experiments based on simulated data, as
well as global path planning experiments based on real world MAV data. We show
that our augmented system, provided with only sparse depth perception, can
reach on-par performance to ground truth depth input in simulated online
planning experiments. On real world MAV data the augmented system demonstrates
superior performance compared to a planner based on very dense RGB-D depth
maps.
