  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.
