  We provide an open-source C++ library for real-time metric-semantic
visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes
beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM,
VINS- Mono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling
in 3D. Kimera is designed with modularity in mind and has four key components:
a visual-inertial odometry (VIO) module for fast and accurate state estimation,
a robust pose graph optimizer for global trajectory estimation, a lightweight
3D mesher module for fast mesh reconstruction, and a dense 3D metric-semantic
reconstruction module. The modules can be run in isolation or in combination,
hence Kimera can easily fall back to a state-of-the-art VIO or a full SLAM
system. Kimera runs in real-time on a CPU and produces a 3D metric-semantic
mesh from semantically labeled images, which can be obtained by modern deep
learning methods. We hope that the flexibility, computational efficiency,
robustness, and accuracy afforded by Kimera will build a solid basis for future
metric-semantic SLAM and perception research, and will allow researchers across
multiple areas (e.g., VIO, SLAM, 3D reconstruction, segmentation) to benchmark
and prototype their own efforts without having to start from scratch.
