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.