A recent paper by members of the DCIST alliance develops 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, VINSMono, 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.
Source: A. Rosinol, M. Abate, Y. Chang, L. Carlone “Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping”, IEEE Int. Conf. Robot. Autom. (ICRA), ArXiv preprint: https://arxiv.org/pdf/1910.02490.pdf, 2020.
Open-source Code: https://github.com/MIT-SPARK/Kimera
Task: RA1.A1 The Swarm’s Knowledge Base: Contextual Perceptual Representations