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

In this paper, we propose a novel LiDAR-Inertial-Visual sensor fusion framework termed R³LIVE, which takes advantage of measurement of LiDAR, inertial, and visual sensors to achieve robust and accurate state estimation. R³LIVE consists of two subsystems, a LiDAR-Inertial odometry (LIO) and a Visual-Inertial odometry (VIO). The LIO subsystem (FAST-LIO) utilizes the measurements from LiDAR and inertial sensors and builds the geometric structure (i.e., the positions of 3D points) of the map. The VIO subsystem uses 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 the visual data directly and effectively by minimizing the frame-to-map photometric error. The proposed system R³LIVE is developed based on our previous work R²LIVE, with a completely different VIO architecture design. The overall system is able to reconstruct the precise, dense, 3D, RGB-colored maps of the surrounding environment in real-time (see our attached video ¹¹https://youtu.be/j5fT8NE5fdg). Our experiments show that the resultant system achieves higher robustness and accuracy in state estimation than its current counterparts. To share our findings and make contributions to the community, we open source R³LIVE on our Github ²²https://github.com/hku-mars/r31ive