Tightly-Coupled LiDAR-IMU-Wheel Odometry With Online Calibration of a Kinematic Model for Skid-Steering Robots

Abstract

Tunnels and long corridors are challenging environments for LiDAR-based odometry estimation algorithms because a LiDAR point cloud should degenerate (i.e., point cloud matching cannot work properly) in such environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm incorporating online calibration of a kinematic model 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. Despite the dynamically changing kinematic parameters (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 point cloud-based constraints sufficiently operate. Furthermore, we estimate the uncertainty (i.e., covariance matrix) of the wheel odometry online for creating a constraint with a reasonable statistical model even in rough terrains. 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 rough outdoor terrain thanks to online uncertainty estimation of wheel odometry. The third experiment shows the proposed online calibration enables robust odometry estimation in a condition that terrains change.

Keywords

• Odometry
• factor graph optimization
• sensor fusion
• online calibration
• point cloud degeneration
• online uncertainty (covariance matrix) estimation