Autonomous aerial navigation in absolute darkness is crucial for post-disaster search and rescue operations, which often occur from disaster-zone power outages. Yet, due to resource constraints, tiny aerial robots, perfectly suited for these operations, are unable to navigate in the darkness to find survivors safely. In this paper, we present an autonomous aerial robot for navigation in the dark by combining an Infra-Red (IR) monocular camera with a large-aperture coded lens and structured light without external infrastructure like GPS or motion-capture. Our approach obtains depth-dependent defocus cues (each structured light point appears as a pattern that is depth dependent), which acts as a strong prior for our AsterNet deep depth estimation model. The model is trained in simulation by generating data using a simple optical model and transfers directly to the real world without any fine-tuning or retraining. AsterNet runs onboard the robot at 20 Hz on an NVIDIA Jetson Orin$^\text{TM}$ Nano. Furthermore, our network is robust to changes in the structured light pattern and relative placement of the pattern emitter and IR camera, leading to simplified and cost-effective construction. We successfully evaluate and demonstrate our proposed depth navigation approach AsterNav using depth from AsterNet in many real-world experiments using only onboard sensing and computation, including dark matte obstacles and thin ropes (diameter 6.25mm), achieving an overall success rate of 95.5% with unknown object shapes, locations and materials. To the best of our knowledge, this is the first work on monocular, structured-light-based quadrotor navigation in absolute darkness.

翻译：在绝对黑暗环境下的自主空中导航对于灾后搜救行动至关重要，这类行动常因灾区电力中断而需要在黑暗中进行。然而，由于资源限制，非常适合此类任务的微型空中机器人无法在黑暗中安全导航以寻找幸存者。本文提出一种用于黑暗环境导航的自主空中机器人，其结合了配备大孔径编码透镜的红外单目相机与结构光，且无需依赖GPS或运动捕捉等外部基础设施。我们的方法获取与深度相关的离焦线索（每个结构光点呈现为一种依赖于深度的图案），这为我们的AsterNet深度估计模型提供了强先验。该模型通过在仿真中使用简单光学模型生成数据进行训练，并可直接迁移至现实世界，无需任何微调或重新训练。AsterNet在机器人搭载的NVIDIA Jetson Orin™ Nano平台上以20 Hz的频率实时运行。此外，我们的网络对结构光图案的变化以及图案发射器与红外相机的相对位置变化具有鲁棒性，从而简化了系统构建并降低了成本。我们通过大量仅使用机载传感与计算的真实世界实验（包括黑暗哑光障碍物和细绳（直径6.25毫米）），成功评估并展示了所提出的AsterNav深度导航方法（使用AsterNet输出的深度信息），在物体形状、位置和材质未知的情况下实现了95.5%的整体成功率。据我们所知，这是首项关于在绝对黑暗环境下基于单目结构光的四旋翼无人机导航的研究工作。