Existing LiDAR-inertial-visual odometry and mapping (LIV-SLAM) systems mainly utilize the LiDAR-inertial odometry (LIO) module for structure reconstruction and the visual-inertial odometry (VIO) module for color rendering. However, the accuracy of VIO is often compromised by photometric changes, weak textures and motion blur, unlike the more robust LIO. This paper introduces SR-LIVO, an advanced and novel LIV-SLAM system employing sweep reconstruction to align reconstructed sweeps with image timestamps. This allows the LIO module to accurately determine states at all imaging moments, enhancing pose accuracy and processing efficiency. Experimental results on two public datasets demonstrate that: 1) our SRLIVO outperforms existing state-of-the-art LIV-SLAM systems in both pose accuracy and time efficiency; 2) our LIO-based pose estimation prove more accurate than VIO-based ones in several mainstream LIV-SLAM systems (including ours). We have released our source code to contribute to the community development in this field.

翻译：现有激光雷达-惯性-视觉里程计与建图（LIV-SLAM）系统主要利用激光雷达-惯性里程计（LIO）模块进行结构重建，并借助视觉-惯性里程计（VIO）模块实现颜色渲染。然而，与鲁棒性更强的LIO相比，VIO的精度常因光度变化、弱纹理和运动模糊而受损。本文提出SR-LIVO，一种采用扫描重构将重构扫描与图像时间戳对齐的先进新型LIV-SLAM系统。该方法使LIO模块能够精确确定所有成像时刻的状态，从而提升位姿精度和处理效率。在两个公开数据集上的实验结果表明：1）我们的SR-LIVO在位姿精度和时间效率上均优于现有最先进的LIV-SLAM系统；2）在多个主流LIV-SLAM系统（含本系统）中，基于LIO的位姿估计比基于VIO的方法更为精确。我们已公开源代码以促进该领域的社区发展。