Abstract
Autonomous mobile robot systems have been extremely useful in exploration tasks for inspection and surveying of unknown environments, where map quality and exploration speed are often important factors. To effectively increase the exploration speed, multi-robot systems and collaborative exploration have been gaining attention in recent years. However, multi-robot exploration introduces two main challenges: 1) shared mapping between the robots; and 2) efficient coordination between the robots. Towards efficient and practical multi-robot exploration, this work proposes a new Distributed Multi-Robot Collaborative SLAM (Multi-SLAM) framework and a Lightweight Predictive Frontier Exploration (LPFE) to enable ground robot fleets to explore unknown environments faster and efficiently. Our Multi-SLAM approach generates a graph based globally optimized map using information from all robots in the environment in a network bandwidth efficient manner, while our LPFE coordinates the exploration of the robots using a deterministic, inference-based heuristic, allowing robots to anticipate one another's actions without explicit communication. The experimental results demonstrate that our pipeline outperforms traditional frontier exploration approach, as well as state-of-the-art planners for ground robots, with up to 70% reduction in exploration times, with up to 13\times less CPU usage and up to 50\times less network bandwidth usage. We also present our Multi-SLAM and LPFE code-base which we have extensively tested in real-world robot fleets in different environments.