Tunnels and long corridors are challenging environments for mobile robots
because a LiDAR point cloud should degenerate in these environments. To tackle
point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel
odometry algorithm with an online calibration for skid-steering robots. We
propose a full linear wheel odometry factor, which not only serves as a motion
constraint but also performs the online calibration of kinematic models for
skid-steering robots. Despite the dynamically changing kinematic model (e.g.,
wheel radii changes caused by tire pressures) and terrain conditions, our
method can address the model error via online calibration. Moreover, our method
enables an accurate localization in cases of degenerated environments, such as
long and straight corridors, by calibration while the LiDAR-IMU fusion
sufficiently operates. Furthermore, we estimate the uncertainty (i.e.,
covariance matrix) of the wheel odometry online for creating a reasonable
constraint. The proposed method is validated through three experiments. The
first indoor experiment shows that the proposed method is robust in severe
degeneracy cases (long corridors) and changes in the wheel radii. The second
outdoor experiment demonstrates that our method accurately estimates the sensor
trajectory despite being in rough outdoor terrain owing to online uncertainty
estimation of wheel odometry. The third experiment shows the proposed online
calibration enables robust odometry estimation in changing terrains.

针对隧道和长走廊等具有挑战性的环境中的移动机器人，本研究提出一种与在线校准相结合的 LiDAR-IMU-wheel 测距法算法，针对滑动转向机器人进行研究，旨在解决点云退化的问题。通过在线校准和不断变化的运动模型，该方法能够在退化环境下实现准确的定位，并通过在线不确定性估计实现合理约束。