This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) algorithm framework that enables a heterogeneous robot team to collaboratively construct object-based metric-semantic maps of 3D environments featuring indoor, urban, and forests without relying on GPS. The framework integrates a data-driven front-end for instance segmentation from either RGBD cameras or LiDARs and a custom back-end for optimizing robot trajectories and object landmarks in the map. To allow multiple robots to merge their information, we design semantics-driven place recognition algorithms that leverage the informativeness and viewpoint invariance of the object-level metric-semantic map for inter-robot loop closure detection. A communication module is designed to track each robot's observations and those of other robots whenever communication links are available. Our framework enables real-time decentralized operations onboard robots, allowing them to opportunistically leverage communication. We integrate the proposed framework with the autonomous navigation and exploration systems of three types of aerial and ground robots, conducting extensive experiments in a variety of indoor and outdoor environments. These experiments demonstrate accuracy in inter-robot localization and object mapping, along with its moderate demands on computation, storage, and communication resources. The framework is open-sourced and available as a modular stack for object-level metric-semantic SLAM, suitable for both single-agent and multi-robot scenarios. The project website and code can be found at https://xurobotics.github.io/slideslam/ and https://github.com/XuRobotics/SLIDE_SLAM, respectively.

翻译：本文提出了一种实时去中心化度量-语义同步定位与建图（SLAM）算法框架，使异构机器人团队能够在无需GPS依赖的情况下，协作构建面向室内、城市及森林等三维环境的基于对象的度量-语义地图。该框架集成了数据驱动的前端模块（用于从RGBD相机或激光雷达进行实例分割）与定制化的后端模块（用于优化机器人轨迹与地图中的对象地标）。为实现多机器人间的信息融合，我们设计了语义驱动的场景识别算法，利用对象级度量-语义地图的信息丰富性与视角不变性进行机器人间的回环检测。同时开发了通信模块，用于在通信链路可用时追踪各机器人自身及其他机器人的观测数据。本框架支持机器人在载设备上实现实时去中心化运行，并能够机会性地利用通信资源。我们将所提框架与三类空中及地面机器人的自主导航与探索系统集成，在多种室内外环境中进行了大量实验。实验结果表明，该系统在机器人间定位与对象建图方面具有较高精度，同时对计算、存储及通信资源的需求较为适中。该框架已开源，可作为模块化工具栈用于对象级度量-语义SLAM，适用于单智能体与多机器人场景。项目网站与代码分别发布于 https://xurobotics.github.io/slideslam/ 与 https://github.com/XuRobotics/SLIDE_SLAM。