# Stronger Together: Air-Ground Robotic Collaboration Using Semantics

arXiv: https://arxiv.org/abs/2206.14289 · HTML: https://mlsanity.com/paper/2206.14289/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2022.

## Built on by (most cited first, 8 of 8)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [Air-Ground Collaboration for Language-Specified Missions in Unknown Environments](https://mlsanity.com/paper/2505.09108/) (2025)
- [Pathfinder for Low-altitude Aircraft with Binary Neural Network](https://mlsanity.com/paper/2409.08824/) (2024)
- [Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities](https://mlsanity.com/paper/2505.09477/) (2025)
- [Learning autonomous driving from aerial imagery](https://mlsanity.com/paper/2410.14177/) (2024)
- [Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response](https://mlsanity.com/paper/2506.04276/) (2025)
- [Systematic Evaluation of Novel View Synthesis for Video Place Recognition](https://mlsanity.com/paper/2603.05876/) (2026)

## Abstract (arXiv)

In this work, we present an end-to-end heterogeneous multi-robot system framework where ground robots are able to localize, plan, and navigate in a semantic map created in real time by a high-altitude quadrotor. The ground robots choose and deconflict their targets independently, without any external intervention. Moreover, they perform cross-view localization by matching their local maps with the overhead map using semantics. The communication backbone is opportunistic and distributed, allowing the entire system to operate with no external infrastructure aside from GPS for the quadrotor. We extensively tested our system by performing different missions on top of our framework over multiple experiments in different environments. Our ground robots travelled over 6 km autonomously with minimal intervention in the real world and over 96 km in simulation without interventions.