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In this letter, we propose a novel LiDAR-Inertial-Visual sensor fusionframework termed R3LIVE, which takes advantage of measurement of LiDAR,inertial, and visual sensors to achieve robust and accurate state estimation.R3LIVE is contained of two subsystems, the LiDAR-inertial odometry (LIO) andvisual-inertial odometry (VIO). The LIO subsystem (FAST-LIO) takes advantage ofthe measurement from LiDAR and inertial sensors and builds the geometrystructure of (i.e. the position of 3D points) global maps. The VIO subsystemutilizes the data of visual-inertial sensors and renders the map's texture(i.e. the color of 3D points). More specifically, the VIO subsystem fuses thevisual data directly and effectively by minimizing the frame-to-map photometricerror. The developed system R3LIVE is developed based on our previous workR2LIVE, with careful architecture design and implementation. Experiment resultsshow that the resultant system achieves more robustness and higher accuracy instate estimation than current counterparts (see our attached video).  R3LIVE is a versatile and well-engineered system toward various possibleapplications, which can not only serve as a SLAM system for real-time roboticapplications, but can also reconstruct the dense, precise, RGB-colored 3D mapsfor applications like surveying and mapping. Moreover, to make R3LIVE moreextensible, we develop a series of offline utilities for reconstructing andtexturing meshes, which further minimizes the gap between R3LIVE and various of3D applications such as simulators, video games and etc (see our demos video).To share our findings and make contributions to the community, we open sourceR3LIVE on our Github, including all of our codes, software utilities, and themechanical design of our device.

R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package