面向滑移转向机器人的带运动模型在线标定的紧耦合激光雷达-IMU-轮式里程计
Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
AI总结：
针对隧道、长走廊等环境下激光雷达点云退化问题，提出带在线标定的紧耦合LiDAR-IMU-轮式里程计算法，通过全线性轮式里程计因子实现运动约束与模型标定，提升退化环境、地形变化下的定位鲁棒性与精度。
摘要：
隧道与长走廊对移动机器人而言是具有挑战性的环境，因为激光雷达点云会在这类环境中发生退化。为解决点云退化问题，本研究提出了一种面向滑移转向机器人的、带在线标定功能的紧耦合LiDAR-IMU-轮式里程计算法。我们提出了一种全线性轮式里程计因子，它不仅可作为运动约束，还能对滑移转向机器人的运动模型进行在线标定。尽管运动模型会动态变化（例如轮胎气压导致的轮半径变化），且地形条件复杂，我们的方法仍可通过在线标定修正模型误差。此外，在LiDAR-IMU融合充分运行的前提下，我们的方法通过标定可在长直走廊等退化环境中实现准确定位。我们还在线估计轮式里程计的不确定性（即协方差矩阵），以构建合理的约束。所提方法通过三项实验得到验证。第一项室内实验表明，该方法在严重退化场景（长走廊）和轮半径变化的情况下均具有鲁棒性。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，即使在崎岖的室外地形中，我们的方法也能准确估计传感器轨迹。第三项实验表明，所提出的在线标定可在地形变化的情况下实现鲁棒的里程计估计。
英文摘要：
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.
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