With the rapid deployment of service robots, a method should be established to allow multiple robots to work in the same place to collaborate and share the spatial information. To this end, we present a collaborative visual simultaneous localization and mapping (SLAM) framework particularly designed for service robot scenarios. 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. To enable real-time information sharing, we design a simple but effective communication pipeline and a novel landmark retrieval method to augment each client's local map with nearby landmarks from the server. 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:随着服务机器人的迅速部署,应当制定一种方法,允许多个机器人在同一地点工作,以合作和分享空间信息。为此目的,我们提出了一个特别为服务机器人情景设计的协同视觉同步本地化和绘图框架(SLAM),一个边端服务器维护地图数据库并进行全球优化,每个机器人都可以对现有地图进行登记,更新地图,或者建造新的地图,所有地图都有统一的界面,低计算和记忆成本。为了能够实时共享信息,我们设计了一个简单而有效的通信管道和一个新的里程碑式检索方法,用服务器附近的地标加强每个客户的本地地图。这个框架很笼统,足以支持 RGB-D 和单人相机,以及多摄像头的机器人,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。