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

Jiarong Lin, Fu Zhang

20222022 International Conference on Robotics and Automation (ICRA)346 citationsDOI

Abstract

In this paper, we propose a novel LiDAR-Inertial-Visual sensor fusion framework termed R 3 LIVE, which takes advantage of measurement of LiDAR, inertial, and visual sensors to achieve robust and accurate state estimation. R 3 LIVE 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 R 3 LIVE is developed based on our previous work R 2 LIVE, 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 1 1 https://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 R 3 LIVE on our Github 2 2 https://github.com/hku-mars/r31ive

Topics & Concepts

Artificial intelligenceComputer scienceLidarComputer visionInertial frame of referenceComputer graphics (images)PhysicsRemote sensingGeographyQuantum mechanicsRobotics and Sensor-Based LocalizationIndoor and Outdoor Localization Technologies3D Surveying and Cultural Heritage

R<sup>3</sup>LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

Abstract

Topics & Concepts