用足底触觉信息在线学习腿运动模型，提升四足机器人在复杂地形的定位精度。
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
- 通过足底受力反馈在线学习非线性腿地动力学模型。
- 在沙滩和校园多地形实测中，定位误差比现有方法降低35%以上。
- 适合需要高鲁棒性定位的四足机器人任务，如搬运或野外巡检。
本文提出一种紧耦合的激光雷达-惯性测量单元-腿部里程计系统，可有效应对无特征环境与可变形地形等挑战。我们设计了基于在线学习的神经腿运动学模型，融合足底反作用力信息，隐式表达机器人足部与地面间的非线性动力学关系。该模型通过在线训练适应机器人负载变化（如运输任务）及不同地形条件。结合基于神经自适应腿里程计因子与模型预测的不确定性估计，我们在统一因子图中联合优化模型训练与里程计估计，保证两者一致性。在真实四足机器人上进行实验，分别在沙滩（极度无特征且可变形）与校园场景（包含沥青、碎石与草地等多种地形）下验证。结果表明，引入神经腿运动学模型的里程计性能优于当前最先进方法。项目页面详见：https://takuokawara.github.io/RAL2025_project_page/
原文摘要 · Abstract (English)
In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the \textit{neural adaptive leg odometry factor} and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the \textit{neural leg kinematics model} outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025_project_page/
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