SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep
  Reconstruction
Zikang Yuan Jie Deng Ruiye Ming Fengtian Lang Xin Yang
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.