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**R3LIVE** is a novel LiDAR-Inertial-Visual sensor fusion framework, which takes advantage of measurement of LiDAR, inertial, and visual sensors to achieve robust and accurate state estimation. R3LIVE is built upon our previous work [R2LIVE](https://github.com/hku-mars/r2live), is contained of two subsystems: the LiDAR-inertial odometry (LIO) and the visual-inertial odometry (VIO). The LIO subsystem ([FAST-LIO](https://github.com/hku-mars/FAST_LIO)) takes advantage of the measurement from LiDAR and inertial sensors and builds the geometric structure of (i.e. the position of 3D points) global maps. The VIO subsystem utilizes the data of visual-inertial sensors and renders the map's texture (i.e. the color of 3D points).
Our preprint paper is available [here](https://github.com/hku-mars/r3live/blob/master/papers/R3LIVE:%20A%20Robust%2C%20Real-time%2C%20RGB-colored%2C%20LiDAR-Inertial-Visual%20tightly-coupled%20stateEstimation%20and%20mapping%20package.pdf), with our accompanying videos are now available on YouTube (click below images to open) and Bilibili[1](https://www.bilibili.com/video/BV1d341117d6?share_source=copy_web), [2](https://www.bilibili.com/video/BV1e3411q7Di?share_source=copy_web).