  In robotic applications, a key requirement for safe and efficient motion
planning is the ability to map obstacle-free space in unknown, cluttered 3D
environments. However, commodity-grade RGB-D cameras commonly used for sensing
fail to register valid depth values on shiny, glossy, bright, or distant
surfaces, leading to missing data in the map. To address this issue, we propose
a framework leveraging probabilistic depth completion as an additional input
for spatial mapping. We introduce a deep learning architecture providing
uncertainty estimates for the depth completion of RGB-D images. Our pipeline
exploits the inferred missing depth values and depth uncertainty to complement
raw depth images and improve the speed and quality of free space mapping.
Evaluations on synthetic data show that our approach maps significantly more
correct free space with relatively low error when compared against using raw
data alone in different indoor environments; thereby producing more complete
maps that can be directly used for robotic navigation tasks. The performance of
our framework is validated using real-world data.
