We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera. Then, when two robots are within communication range, they initiate a distributed place recognition and robust pose graph optimization protocol with a novel incremental maximum clique outlier rejection; the protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations and real data. Kimera-Multi (i) is able to build accurate 3D metric-semantic meshes, (ii) is robust to incorrect loop closures while requiring less computation than state-of-the-art distributed SLAM back-ends, and (iii) is efficient, both in terms of computation at each robot as well as communication bandwidth.

中文翻译：

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Kimera-Multi：分布式多机器人度量语义同时定位和映射系统

我们展示了第一个用于密集度量语义同步定位和映射 (SLAM) 的完全分布式多机器人系统。我们的系统被称为 Kimera-Multi，由配备视觉惯性传感器的机器人团队实施，并实时构建环境的 3D 网格模型，其中网格的每个面都用语义标签（例如、建筑、道路、物体）。在 Kimera-Multi 中，每个机器人使用 Kimera 构建局部轨迹估计和局部网格。然后，当两个机器人在通信范围内时，它们会启动分布式位置识别和鲁棒姿势图优化协议，并使用新颖的增量最大集团异常值拒绝；该协议允许机器人通过利用机器人间闭环来改进其局部轨迹估计。最后，每个机器人使用其改进的轨迹估计来使用网格变形技术校正局部网格。我们在逼真的模拟和真实数据中展示了 Kimera-Multi。Kimera-Multi (i) 能够构建准确的 3D 度量语义网格，(ii) 对不正确的闭环具有鲁棒性，同时比最先进的分布式 SLAM 后端需要更少的计算，并且 (iii) 高效，在每个机器人的计算以及通信带宽方面。