LIC-Fusion: LiDAR-Inertial-Camera Odometry
This work addresses odometry estimation for robotics and autonomous systems, offering an incremental improvement by integrating LiDAR into existing visual-inertial frameworks.
The paper tackles the problem of multi-sensor odometry by proposing LIC-Fusion, which tightly fuses LiDAR, inertial, and camera data with online calibration, resulting in improved accuracy and robustness over state-of-the-art methods in indoor and outdoor environments.
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.