# Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV

arXiv: https://arxiv.org/abs/1609.00017 · HTML: https://mlsanity.com/paper/1609.00017/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [Tetris-inspired detector with neural network for radiation mapping](https://mlsanity.com/paper/2302.07099/) (2023)
- [Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response](https://mlsanity.com/paper/2506.04276/) (2025)
- [RADRON: Cooperative Localization of Ionizing Radiation Sources by MAVs with Compton Cameras](https://mlsanity.com/paper/2510.26018/) (2025)

## Abstract (arXiv)

Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.