A Collaborative Visual SLAM Framework for Service Robots
This framework addresses the problem of efficient and scalable SLAM for service robots operating in shared environments, allowing for collaborative map building and localization.
This paper proposes a collaborative visual SLAM framework for service robots, where an edge server manages map data and global optimization. Robots can register, update, or build maps with low computational and memory overhead, leveraging a novel landmark organization and retrieval method for real-time information sharing.
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. We design an elegant communication pipeline to enable real-time information sharing between robots. With a novel landmark organization and retrieval method on the server, each robot can acquire landmarks predicted to be in its view, to augment its local map. The framework is general enough to support both RGB-D and monocular cameras, as well as robots with multiple cameras, taking the rigid constraints between cameras into consideration. The proposed framework has been fully implemented and verified with public datasets and live experiments.