  In this letter, we propose a robust, real-time tightly-coupled multi-sensor
fusion framework, which fuses measurement from LiDAR, inertial sensor, and
visual camera to achieve robust and accurate state estimation. Our proposed
framework is composed of two parts: the filter-based odometry and factor graph
optimization. To guarantee real-time performance, we estimate the state within
the framework of error-state iterated Kalman-filter, and further improve the
overall precision with our factor graph optimization. Taking advantage of
measurement from all individual sensors, our algorithm is robust enough to
various visual failure, LiDAR-degenerated scenarios, and is able to run in
real-time on an on-board computation platform, as shown by extensive
experiments conducted in indoor, outdoor, and mixed environment of different
scale. Moreover, the results show that our proposed framework can improve the
accuracy of state-of-the-art LiDAR-inertial or visual-inertial odometry. To
share our findings and to make contributions to the community, we open source
our codes on our Github.
