# Search and Rescue under the Forest Canopy using Multiple UAVs

arXiv: https://arxiv.org/abs/1908.10541 · HTML: https://mlsanity.com/paper/1908.10541/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy](https://mlsanity.com/paper/2109.06479/) (2021)
- [Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems](https://mlsanity.com/paper/2404.06413/) (2024)
- [Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2403.06621/) (2024)
- [ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue](https://mlsanity.com/paper/2504.01261/) (2025)
- [A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments](https://mlsanity.com/paper/2107.08834/) (2021)
- [VISTA: Monocular Segmentation-Based Mapping for Appearance and View-Invariant Global Localization](https://mlsanity.com/paper/2507.11653/) (2025)
- [Robots as Tokens: Unified Diffusion Transformer for Coordinated Multi-Robot Trajectory Generation](https://mlsanity.com/paper/2606.15550/) (2026)

## Abstract (arXiv)

We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared to the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.