  LiDAR-inertial odometry (LIO), which fuses complementary information of a
LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for
state estimation. In LIO, both pose and velocity are regarded as state
variables that need to be solved. However, the widely-used Iterative Closest
Point (ICP) algorithm can only provide constraint for pose, while the velocity
can only be constrained by IMU pre-integration. As a result, the velocity
estimates inclined to be updated accordingly with the pose results. In this
paper, we propose LIWO, an accurate and robust LiDAR-inertialwheel (LIW)
odometry, which fuses the measurements from LiDAR, IMU and wheel encoder in a
bundle adjustment (BA) based optimization framework. The involvement of a wheel
encoder could provide velocity measurement as an important observation, which
assists LIO to provide a more accurate state prediction. In addition,
constraining the velocity variable by the observation from wheel encoder in
optimization can further improve the accuracy of state estimation. Experiment
results on two public datasets demonstrate that our system outperforms all
state-of-the-art LIO systems in terms of smaller absolute trajectory error
(ATE), and embedding a wheel encoder can greatly improve the performance of LIO
based on the BA framework.
