Stronger Together: Air-Ground Robotic Collaboration Using Semantics

Abstract

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.

Real-Time Uav 3d Image Point Clouds Mapping

“…OpenREALM (Kern et al, 2020) can acquire mosaics or 3D surface information with different modes of operation. Additionally, Miller et al propose a mapping algorithm ASOOM (Miller et al, 2022) that can generate maps in the GridMap format in real-time for collaborative air-ground missions.…”