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.

中文翻译：

---

具有概率深度的体积占用映射与机器人导航

在机器人应用中，安全有效的运动计划的关键要求是能够在未知的，混乱的3D环境中绘制无障碍空间。但是，通常用于感应的商品级RGB-D相机无法在有光泽，有光泽，明亮或远处的表面上记录有效的深度值，从而导致地图中的数据丢失。为了解决这个问题，我们提出了一个框架，利用概率深度完成作为空间映射的附加输入。我们引入了深度学习体系结构，该体系结构为RGB-D图像的深度完成提供不确定性估计。我们的管道利用推断出的缺失深度值和深度不确定性来补充原始深度图像，并提高自由空间贴图的速度和质量。对合成数据的评估表明，与在不同的室内环境中单独使用原始数据相比，我们的方法可显着映射更正确的自由空间，并且误差相对较低；从而生成可以直接用于机器人导航任务的更完整的地图。我们的框架的性能已使用实际数据进行了验证。