確率的深度補完を用いたロボットナビゲーション向けボリューメトリック占有マッピング
Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation
RGB-Dカメラで欠損した深度を不確実性付きで深層学習により補完し、自由空間マップの速度と品質を向上させる枠組みを提案した。
著者: Marija Popovic, Florian Thomas, Sotiris Papatheodorou, Nils Funk, Teresa Vidal-Calleja, Stefan Leutenegger
分類: cs.RO
原文アブストラクト
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.
