论文标题
r $^3 $ live ++：一个可靠的，实时的，辐射重建套件，具有紧密耦合的激光惯性式状态估算器
R$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator
论文作者
论文摘要
同时定位和映射（SLAM）对于自动驾驶机器人（例如自动驾驶汽车，自动驾驶无人机），3D映射系统和AR/VR应用至关重要。这项工作提出了一个新颖的LIDAR惯性 - 视觉融合框架，称为r $^3 $ live ++，以实现强大而准确的状态估计，同时可以随时重建辐射图。 R $^3 $ LIVE ++由LIDAR惯性探射仪（LIO）和视觉惯性式（VIO）组成，均为实时运行。 LIO子系统利用LiDAR的测量值重建几何结构（即3D点的位置），而VIO子系统同时从输入图像中同时恢复了几何结构的辐射信息。 r $^3 $ live ++是根据r $^3 $ live开发的，并通过考虑相机光度校准（例如，非线性响应功能和镜头启动）以及相机暴露时间的在线估计，进一步提高了本地化和映射的准确性。我们对公共和私人数据集进行了更广泛的实验，以将我们提出的系统与其他最先进的SLAM系统进行比较。定量和定性结果表明，我们所提出的系统在准确性和鲁棒性方面对其他系统都有显着改善。此外，为了证明我们的工作的可扩展性，{我们根据重建的辐射图开发了多个应用程序，例如高动态范围（HDR）成像，虚拟环境探索和3D视频游戏。}最后，分享我们的发现并为社区分享我们的发现并为社区做出贡献，我们在我们的代码，硬件设计，硬件设计和数据上可用：
Simultaneous localization and mapping (SLAM) are crucial for autonomous robots (e.g., self-driving cars, autonomous drones), 3D mapping systems, and AR/VR applications. This work proposed a novel LiDAR-inertial-visual fusion framework termed R$^3$LIVE++ to achieve robust and accurate state estimation while simultaneously reconstructing the radiance map on the fly. R$^3$LIVE++ consists of a LiDAR-inertial odometry (LIO) and a visual-inertial odometry (VIO), both running in real-time. The LIO subsystem utilizes the measurements from a LiDAR for reconstructing the geometric structure (i.e., the positions of 3D points), while the VIO subsystem simultaneously recovers the radiance information of the geometric structure from the input images. R$^3$LIVE++ is developed based on R$^3$LIVE and further improves the accuracy in localization and mapping by accounting for the camera photometric calibration (e.g., non-linear response function and lens vignetting) and the online estimation of camera exposure time. We conduct more extensive experiments on both public and our private datasets to compare our proposed system against other state-of-the-art SLAM systems. Quantitative and qualitative results show that our proposed system has significant improvements over others in both accuracy and robustness. In addition, to demonstrate the extendability of our work, {we developed several applications based on our reconstructed radiance maps, such as high dynamic range (HDR) imaging, virtual environment exploration, and 3D video gaming.} Lastly, to share our findings and make contributions to the community, we make our codes, hardware design, and dataset publicly available on our Github: github.com/hku-mars/r3live
