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.
翻译:我们为服务机器人提供了一个合作视觉同步本地化和绘图框架(SLAM)框架(SLAM ) 。 通过一个边端服务器维护地图数据库并进行全球优化,每个机器人都可以在现有的地图上登记、更新地图或建造新的地图,所有地图都有一个统一的界面,计算和记忆成本低廉。我们设计了一个优雅的通信管道,以便机器人之间实时共享信息。有了一个新的里程碑式组织和服务器上的检索方法,每个机器人可以获取预计在它眼中的地标,以扩充其本地地图。这个框架很笼统,足以支持 RGB-D 和单镜相机,以及多照相机的机器人,同时考虑到摄像头之间的严格限制。 拟议的框架已经完全实施,并经过公共数据集和现场实验的验证。