Air-Ground Collaboration With SPOMP: Semantic Panoramic Online Mapping and Planning

Abstract

Mapping and navigation have gone hand-in-hand since long before robots existed. Maps are a key form of communication, allowing someone who has never been somewhere to nonetheless navigate that area successfully. In the context of multirobot systems, the maps and information that flow between robots are necessary for effective collaboration, whether those robots are operating concurrently, sequentially, or completely asynchronously. In this article, we argue that maps must go beyond encoding purely geometric or visual information to enable increasingly complex autonomy, particularly between robots. We propose a framework for multirobot autonomy, focusing in particular on air and ground robots operating in outdoor 2.5-D environments. We show that semantic maps can enable the specification, planning, and execution of complex collaborative missions, including localization in Global Positioning System (GPS)-denied settings. A distinguishing characteristic of this work is that we strongly emphasize field experiments and testing, and by doing so demonstrate that these ideas can work at scale in the real world. We also perform extensive simulation experiments to validate our ideas at even larger scales. We believe that these experiments and the experimental results constitute a significant step forward toward advancing the state of the art of large-scale, collaborative multirobot systems operating with real communication, navigation, and perception constraints.