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Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
- Researchers introduce tightly coupled LiDAR-IMU-leg odometry for challenging conditions like featureless environments and deformable terrains.
- They employ an online learning-based leg kinematics model called the neural leg kinematics model, which incorporates foot tactile information.
- The model captures the nonlinear dynamics between robot feet and the ground, enhancing adaptability to weight load changes and terrain conditions.
- Online training of the model ensures adaptability to different scenarios like delivery or transportation tasks.
- The extit{neural adaptive leg odometry factor} and online uncertainty estimation are used for training the kinematics model and odometry estimation on a unified factor graph.
- Real experiments with a quadruped robot in challenging scenarios such as a sandy beach and a campus with various terrains validate the effectiveness of the proposed method.
- The odometry estimation incorporating the extit{neural leg kinematics model} performs better than existing state-of-the-art methods.
- The researchers offer a project page for further information: https://takuokawara.github.io/RAL2025_project_page/
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