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 observations and those of other robots within the communication range. 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 localization error of 0.22 meters in position and -0.16 degrees in orientation, an object mapping F1 score of 0.92, and a communication packet size of merely 2-3 megabytes per kilometer trajectory with 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方法，该方法利用稀疏、轻量化的基于物体的环境表示，使异构机器人团队能够在无需全球定位系统的条件下，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，包括高层稀疏物体模型语义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，我们实现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闭环检测。本文设计了一个通信模块，用于跟踪各机器人自身及通信范围内其他机器人的观测数据，并利用这些观测构建融合地图。该框架支持机器人在机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：系统平均定位误差为位置0.22米、航向角-0.16度，物体建图F1分数达0.92，在包含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/。