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In robotic applications, a key requirement for safe and efficient motionplanning is the ability to map obstacle-free space in unknown, cluttered 3Denvironments. However, commodity-grade RGB-D cameras commonly used for sensingfail to register valid depth values on shiny, glossy, bright, or distantsurfaces, leading to missing data in the map. To address this issue, we proposea framework leveraging probabilistic depth completion as an additional inputfor spatial mapping. We introduce a deep learning architecture providinguncertainty estimates for the depth completion of RGB-D images. Our pipelineexploits the inferred missing depth values and depth uncertainty to complementraw depth images and improve the speed and quality of free space mapping.Evaluations on synthetic data show that our approach maps significantly morecorrect free space with relatively low error when compared against using rawdata alone in different indoor environments; thereby producing more completemaps that can be directly used for robotic navigation tasks. The performance ofour framework is validated using real-world data.

Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation