Code and data from: milliWatt ultrasound for navigation in visually degraded environments on palm-sized aerial robots

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 GPS-denied wild scenes. Commonly used sensors for obstacle avoidance become ineffective in visually degraded conditions such as low visibility, dust, fog, or complete darkness. 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 ideas to combat the low SNR: Physical noise reduction and a deep learning based denoising method. Firstly, we find an optimal and practical way to block propeller-induced noise. Secondly, we generate and train a neural network to denoise signals. For the first time ever, we enable a palm-sized aerial robot to navigate in visually degraded conditions with thin and transparent obstacles using only on-board sensing and computation. This repository contains the dataset and trained model for the Saranga neural network. The associated code is published as a linked Zenodo record. The contents are organized in chronological order of the pipeline: Dataset (Dryad) 1. Generated dataset used for training and evaluation. 2. Trained model checkpoints for the Saranga network.; Code (Zenodo): 1. Dataset generator script. 2. Training code for the Saranga network. 3. TensorFlow Lite quantization and Edge TPU compilation scripts for Saranga. 4. ROS2 Humble source codes for our onboard autonomy stack including drivers for ICU30201 ultrasonic sensors, perception and planning nodes, along with a MAVLink node for commanding the aerial robot. 5. Benchmark scripts to compare Saranga against classical methodologies. Detailed instructions are provided in the readme.md included in this repository.