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.

SR-LIVO是一种先进的LIV-SLAM系统，使用扫描重建来将重建扫描与图像时间戳对齐，从而允许LIO模块准确确定所有成像时刻的状态，提高姿态准确性和处理效率。通过两个公共数据集的实验结果表明：1）我们的SRLIVO在姿态准确性和时间效率方面优于现有的最先进LIV-SLAM系统；2）我们基于LIO的姿态估计在几个主流LIV-SLAM系统中（包括我们自己的系统）比基于VIO的估计更准确。我们已经发布了我们的源代码，以促进该领域的社区发展。

SR-LIVO：激光雷达-惯性-视觉里程计与重建