  Multi-sensor fusion of multi-modal measurements from commodity inertial,
visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation
holds great potential in robotics and beyond. In this paper, building upon our
prior work (i.e., LIC-Fusion), we develop a sliding-window filter based
LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e.,
LIC-Fusion 2.0), which introduces a novel sliding-window plane-feature tracking
for efficiently processing 3D LiDAR point clouds. In particular, after motion
compensation for LiDAR points by leveraging IMU data, low-curvature planar
points are extracted and tracked across the sliding window. A novel outlier
rejection criterion is proposed in the plane-feature tracking for high-quality
data association. Only the tracked planar points belonging to the same plane
will be used for plane initialization, which makes the plane extraction
efficient and robust. Moreover, we perform the observability analysis for the
LiDAR-IMU subsystem and report the degenerate cases for spatiotemporal
calibration using plane features. While the estimation consistency and
identified degenerate motions are validated in Monte-Carlo simulations,
different real-world experiments are also conducted to show that the proposed
LIC-Fusion 2.0 outperforms its predecessor and other state-of-the-art methods.
