  This paper presents a tightly-coupled multi-sensor fusion algorithm termed
LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU
measurements, sparse visual features, and extracted LiDAR points. In
particular, the proposed LIC-Fusion performs online spatial and temporal sensor
calibration between all three asynchronous sensors, in order to compensate for
possible calibration variations. The key contribution is the optimal (up to
linearization errors) multi-modal sensor fusion of detected and tracked sparse
edge/surf feature points from LiDAR scans within an efficient MSCKF-based
framework, alongside sparse visual feature observations and IMU readings. We
perform extensive experiments in both indoor and outdoor environments, showing
that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial
odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and
robustness to aggressive motions.
