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Accurate Odometry Estimation for Skid-Steering Robots in Challenging Environments with Online Calibration of Kinematic Model


Core Concepts
The proposed method enables accurate odometry estimation for skid-steering robots in challenging environments with point cloud degeneration by tightly coupling LiDAR, IMU, and wheel odometry with online calibration of the kinematic model.
Abstract
The study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm with online calibration of the kinematic model for skid-steering robots. This is formulated as a factor graph optimization problem. Key highlights: Proposed a full linear wheel odometry factor that not only serves as a motion constraint but also performs online calibration of the kinematic parameters, addressing model errors and terrain changes. Estimated the uncertainty of the wheel odometry online and incorporated it into the full linear wheel odometry factor to adapt to different ground surface conditions. Validated the method through three experiments: The indoor experiment showed the method is robust to severe point cloud degeneration in long corridors and changes in wheel radii. The outdoor experiment demonstrated accurate trajectory estimation despite rough terrain, thanks to the online uncertainty estimation. The third experiment showed the online calibration enables robust odometry estimation in changing terrains. The proposed method outperformed state-of-the-art LiDAR-IMU odometry and ablation studies, demonstrating the effectiveness of the online calibration and uncertainty estimation.
Stats
The robot traveled about 120 m in the indoor environment. The robot traveled about 64 m in the outdoor environment. The robot traveled about 144 m in the environment with transition from bricks to outdoor stone tiles, and indoor stone tiles.
Quotes
"Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments." "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." "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."

Deeper Inquiries

How could the proposed method be extended to handle more complex kinematic models, such as those considering nonlinear wheel slippage

To extend the proposed method to handle more complex kinematic models, such as those considering nonlinear wheel slippage, several adjustments and enhancements could be made. One approach could involve incorporating a more sophisticated wheel slippage model that accounts for varying levels of slip in different terrain conditions. This could involve integrating additional sensors or feedback mechanisms to detect and quantify wheel slippage accurately. Furthermore, the calibration process could be expanded to include parameters specific to nonlinear wheel slippage, allowing the system to adapt and optimize its performance based on the current slip conditions. By incorporating these elements, the system could provide more accurate and reliable odometry estimates in scenarios with nonlinear wheel slippage.

What are the potential limitations of the online calibration approach, and how could they be addressed in future work

While the online calibration approach offers significant advantages in terms of adaptability and robustness, there are potential limitations that need to be considered. One limitation is the computational complexity of continuously calibrating the kinematic model online, which could impact real-time performance in highly dynamic environments. This could be addressed by optimizing the calibration process to reduce computational overhead or implementing parallel processing techniques to enhance efficiency. Additionally, the online calibration approach may require a certain level of initial calibration or tuning to ensure accurate convergence, which could be a limitation in scenarios where prior information is limited. Future work could focus on developing more automated or self-initializing calibration methods to mitigate this limitation and improve the system's overall usability and reliability.

How could the insights from this work on robust odometry estimation be applied to other robotic platforms or domains beyond mobile robots

The insights gained from this work on robust odometry estimation for mobile robots could be applied to other robotic platforms and domains to enhance navigation and localization capabilities. For instance, in autonomous aerial vehicles, integrating similar tightly-coupled sensor fusion techniques with online calibration could improve position estimation accuracy in GPS-denied environments or challenging weather conditions. In the context of autonomous underwater vehicles, leveraging the proposed method could enhance localization performance in underwater environments with limited visibility. Furthermore, in the field of industrial robotics, applying the principles of online calibration and uncertainty estimation could optimize robot path planning and execution, leading to increased efficiency and safety in manufacturing processes. By adapting and extending the concepts from this work, a wide range of robotic platforms and domains could benefit from more robust and accurate odometry estimation.
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