Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation

Abstract

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.

arXiv comment8 pages, 10 figures, submission to IEEE Robotics and Automation Letters (revised)

References in corpus (4)

• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Confidence Propagation through CNNs for Guided Sparse Depth Regression
• Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
• Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning

Cited by in corpus (1)

• Depth-based Sampling and Steering Constraints for Memoryless Local Planners