This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) approach that leverages a sparse and lightweight object-based representation to enable a heterogeneous robot team to autonomously explore 3D environments featuring indoor, urban, and forested areas without relying on GPS. We use a hierarchical metric-semantic representation of the environment, including high-level sparse semantic maps of object models and low-level voxel maps. We leverage the informativeness and viewpoint invariance of the high-level semantic map to obtain an effective semantics-driven place-recognition algorithm for inter-robot loop closure detection across aerial and ground robots with different sensing modalities. A communication module is designed to track each robot's own observations and those of other robots whenever communication links are available. Such observations are then used to construct a merged map. Our framework enables real-time decentralized operations onboard robots, allowing them to opportunistically leverage communication. We integrate and deploy our proposed framework on three types of aerial and ground robots. Extensive experimental results show an average inter-robot localization error of approximately 20 cm in position and 0.2 degrees in orientation, an object mapping F1 score consistently over 0.9, and a communication packet size of merely 2-3 megabytes per kilometer trajectory with as many as 1,000 landmarks. The project website can be found at https://xurobotics.github.io/slideslam/.

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）方法，该方法利用稀疏且轻量化的基于对象的场景表示，使异构机器人团队能够在无需依赖GPS的情况下，自主探索包含室内、城市及森林区域的复杂三维环境。我们采用分层的度量-语义环境表示，包括高层稀疏语义对象模型地图与低层体素地图。通过利用高层语义地图的信息丰富性与视角不变性，我们设计了一种高效的语义驱动地点识别算法，用于在不同感知模态的空中与地面机器人之间进行跨机器人回环检测。系统设计了通信模块，可在通信链路可用时跟踪各机器人自身的观测信息及其他机器人的观测信息，并利用这些观测构建融合地图。所提出的框架支持在机器人端进行实时去中心化运算，使其能够机会性地利用通信资源。我们将该框架集成并部署于三类空中与地面机器人平台。大量实验结果表明：系统平均跨机器人定位误差约为位置20厘米、朝向0.2度；对象建图F1分数持续高于0.9；在包含多达1000个路标点的每公里轨迹中，通信数据包大小仅为2-3兆字节。项目网站详见 https://xurobotics.github.io/slideslam/。