Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

Multi-robot simultaneous localization and mapping (SLAM) is a crucial capability to obtain timely situational awareness over large areas. Real-world applications demand multi-robot SLAM systems to be robust to perceptual aliasing and to operate under limited communication bandwidth; moreover, it is desirable for these systems to capture semantic information to enable high-level decision-making and spatial artificial intelligence. This article presents
$ \mathsf{{Kimera-Multi}} $
, a multi-robot system that: 1) is robust and capable of identifying and rejecting incorrect inter- and intrarobot loop closures resulting from perceptual aliasing; 2) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping; and 3) builds a globally consistent metric-semantic 3-D mesh model of the environment in real time, where faces of the mesh are annotated with semantic labels.
$ \mathsf{{Kimera-Multi}} $
is implemented by a team of robots equipped with visual-inertial sensors. Each robot builds a local trajectory estimate and a local mesh using
$ \mathsf{{Kimera}} $
. When communication is available, robots initiate a distributed place recognition and robust pose graph optimization protocol based on a distributed graduated nonconvexity algorithm. The proposed protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures while being robust to outliers. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate
$ \mathsf{{Kimera-Multi}} $
in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots. Both real and simulated experiments involve long trajectories (e.g., up to 800 m per robot). The experiments show that
$ \mathsf{{Kimera-Multi}} $
: 1) outperforms the state of the art in terms of robustness and accuracy; 2) achieves estimation errors comparable to a centralized SLAM system while being fully distributed; 3) is parsimonious in terms of communication bandwidth; 4) produces accurate metric-semantic 3-D meshes; and 5) is modular and can also be used for standard 3-D reconstruction (i.e., without semantic labels) or for trajectory estimation (i.e., without reconstructing a 3-D mesh).