Stronger Together: Air-Ground Robotic Collaboration Using Semantics
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jun 2022.
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Citing papers per year: 2024: 4; 2025: 3; 2026: 1.
8 papers.
- 2024General Place Recognition Survey: Towards Real-World Autonomycited by 23
- 2024SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environmentscited by 18
- 2025Air-Ground Collaboration for Language-Specified Missions in Unknown Environmentscited by 11
- 2024Pathfinder for Low-altitude Aircraft with Binary Neural Networkcited by 4
- 2025Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunitiescited by 3
- 2024Learning autonomous driving from aerial imagerycited by 2
- 2025Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Responsecited by 0
- 2026Systematic Evaluation of Novel View Synthesis for Video Place Recognitioncited by 0
- 2026Systematic Evaluation of Novel View Synthesis for Video Place Recognition6 Mar 2026
- 2025Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response4 Jun 2025
- 2025Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities14 May 2025
- 2025Air-Ground Collaboration for Language-Specified Missions in Unknown Environments14 May 2025
- 2024Learning autonomous driving from aerial imagery18 Oct 2024
- 2024SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments3 Oct 2024
- 2024Pathfinder for Low-altitude Aircraft with Binary Neural Network13 Sep 2024
- 2024General Place Recognition Survey: Towards Real-World Autonomy8 May 2024