Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle avoidance use cameras and LIght Detection And Ranging (LIDAR), which become ineffective in visually degraded conditions such as low visibility, dust, fog or darkness. Other sensors, such as RAdio Detection And Ranging (RADAR), have high power consumption, making them unsuitable for tiny aerial robots. Inspired by bats, we propose Saranga, a low-power ultrasound-based perception stack that localizes obstacles using a dual sonar array. We present two key solutions to combat the low Peak Signal-to-Noise Ratio of $-4.9$ decibels: physical noise reduction and a deep learning based denoising method. Firstly, we present a practical way to block propeller induced ultrasound noise on the weak echoes. The second solution is to train a neural network to utilize the \textcolor{black}{long horizon of ultrasound echoes} for finding signal patterns under high amounts of uncorrelated noise where classical methods were insufficient. We generalize to the real world by using a synthetic data generation pipeline and limited real noise data for training. We enable a palm-sized aerial robot to navigate in visually degraded conditions of dense fog, darkness, and snow in a cluttered environment with thin and transparent obstacles using only on-board sensing and computation. We provide extensive real world results to demonstrate the efficacy of our approach.

翻译：手掌尺寸的微型飞行机器人在狭小杂乱环境中具有卓越的敏捷性和成本效益。然而，其有限的载荷能力直接制约了机载传感套件的配置，从而限制了其在全球定位系统（GPS）拒止的野外场景中执行关键导航任务的能力。常见的避障方法使用摄像头和光探测与测距（LIDAR），但在低能见度、灰尘、雾霾或黑暗等视觉退化条件下会失效。而无线电探测与测距（RADAR）等其他传感器功耗较高，不适用于微型飞行机器人。受蝙蝠启发，我们提出Saranga——一种基于低功耗超声波的感知堆栈，通过双声纳阵列定位障碍物。我们提出两种关键解决方案以应对-4.9分贝的低峰值信噪比：物理降噪与基于深度学习的去噪方法。首先，我们提出一种实用方法，在微弱回波中屏蔽螺旋桨引起的超声波噪声。其次，我们训练神经网络利用超声波回波的长时域特性，在经典方法无法处理的强不相关噪声中寻找信号模式。通过合成数据生成流水线和有限真实噪声数据训练，我们实现了向现实世界的泛化。我们使手掌尺寸飞行机器人仅靠机载传感与计算，即可在浓雾、黑暗和积雪等视觉退化环境中，穿越布满薄且透明障碍物的杂乱空间。我们提供大量真实世界实验结果以证明本方法的有效性。