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.

翻译：隧道和长走廊对移动机器人而言是具有挑战性的环境，因为在这些环境中激光雷达点云会出现退化。为应对点云退化问题，本研究提出了一种面向滑移转向机器人的紧耦合LiDAR-IMU-轮式里程计算法，并具备在线标定功能。我们提出了一种全线性轮式里程计因子，该因子不仅可作为运动约束，还能对滑移转向机器人的运动学模型进行在线标定。尽管运动学模型（例如由轮胎气压变化引起的车轮半径变化）和地形条件动态变化，我们的方法仍能通过在线标定解决模型误差。此外，该方法能够在LiDAR-IMU融合充分运行的同时，通过标定实现退化环境（如长直走廊）下的精确定位。我们还对轮式里程计的不确定性（即协方差矩阵）进行在线估计，以构建合理的约束。通过三项实验验证了所提方法的有效性。第一项室内实验表明，该方法在严重退化场景（长走廊）及车轮半径变化情况下具有鲁棒性。第二项室外实验证明，得益于轮式里程计的在线不确定性估计，即使在不平坦的室外地形中，该方法也能准确估计传感器轨迹。第三项实验显示，所提出的在线标定功能可在变化地形中实现鲁棒的里程计估计。