  With robots being deployed in increasingly complex environments like
underground mines and planetary surfaces, the multi-sensor fusion method has
gained more and more attention which is a promising solution to state
estimation in the such scene. The fusion scheme is a central component of these
methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry
system is presented, which utilizes a degeneration-aware and modular
sensor-fusion pipeline that takes both LiDAR points and relative pose from
another odometry as the measurement in the update process only when
degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and
simulation test are used to demonstrate the higher accuracy of our method
compared to methods using a single observation. Furthermore, the proposed
system is evaluated in perceptually challenging datasets against various
state-of-the-art sensor-fusion methods. The results show that the proposed
system achieves real-time and high estimation accuracy performance despite the
challenging environment and poor observations.
