# A 360-Degree Vision Dataset for Learning Yaw Control on GPS-Denied Micro-UAVs in Disaster-Response-Relevant Environments

arXiv: https://arxiv.org/abs/2608.19866 · HTML: https://mlsanity.com/paper/2608.19866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Revisiting Small Batch Training for Deep Neural Networks](https://mlsanity.com/paper/1804.07612/) (2018)
- [A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03642/) (2019)
- [Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness](https://mlsanity.com/paper/1705.02550/) (2017)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [Towards Resilient Autonomous Navigation of Drones](https://mlsanity.com/paper/2008.09679/) (2020)
- [OmniNxt: A Fully Open-source and Compact Aerial Robot with Omnidirectional Visual Perception](https://mlsanity.com/paper/2403.20085/) (2024)
- [Lessons from Robot-Assisted Disaster Response Deployments by the German Rescue Robotics Center Task Force](https://mlsanity.com/paper/2212.09354/) (2022)
- [Deployment of Aerial Robots after a major fire of an industrial hall with hazardous substances, a report](https://mlsanity.com/paper/2111.14542/) (2021)
- [Drone Path-Following in GPS-Denied Environments using Convolutional Networks](https://mlsanity.com/paper/1905.01658/) (2019)
- [Redefining Recon: Bridging Gaps with UAVs, 360 degree Cameras, and Neural Radiance Fields](https://mlsanity.com/paper/2401.06143/) (2023)

## Abstract (arXiv)

This paper presents a novel data-driven approach to camera-based autonomy for micro-drones in GPS-denied, radio-challenging indoor environments. The target application is disaster and emergency response, where micro-UAVs can provide rapid situational awareness in hazardous settings such as firefighting and chemical, biological, radiological, and nuclear (CBRN) incidents while reducing risk for human responders. When the communication link is lost, the micro-drone uses a learned yaw controller to autonomously navigate toward open space, preserving onboard sensor data that would otherwise be lost with the vehicle. A custom micro-drone equipped with a 360-degree camera was used to record diverse industrial, underground, and training scenarios representative of communication-denied field operations. We introduce a preprocessing pipeline that converts equirectangular 360-degree footage into planar front views and dynamically generates image-label pairs for AI training. We then train and compare multiple convolutional neural network variants that predict a continuous yaw command from a single monocular view. Evaluation on a held-out test set confirms the feasibility of the learned yaw-prediction approach. A semi-autonomous real-world test further demonstrates the practicality of the method while revealing key failure modes, particularly reflections and glare.