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We present a collaborative visual simultaneous localization and mapping(SLAM) framework for service robots. With an edge server maintaining a mapdatabase and performing global optimization, each robot can register to anexisting map, update the map, or build new maps, all with a unified interfaceand low computation and memory cost. We design an elegant communicationpipeline to enable real-time information sharing between robots. With a novellandmark organization and retrieval method on the server, each robot canacquire landmarks predicted to be in its view, to augment its local map. Theframework is general enough to support both RGB-D and monocular cameras, aswell as robots with multiple cameras, taking the rigid constraints betweencameras into consideration. The proposed framework has been fully implementedand verified with public datasets and live experiments.

A Collaborative Visual SLAM Framework for Service Robots