AutoOdom: 为腿足运动学习自回归本体里程计
AutoOdom: Learning Auto-regressive Proprioceptive Odometry for Legged Locomotion
AI总结：
AutoOdom通过自回归训练方法，有效解决腿足运动中本体里程计的仿真到现实转移问题，显著提升导航精度和鲁棒性。
摘要：
准确的本体里程计对于在GPS拒止和视觉退化环境中导航腿足机器人至关重要，这些环境传统视觉里程计系统失效。当前方法面临关键限制：分析滤波方法因建模不确定性导致累积漂移，混合学习-滤波方法受限于其分析组件，而纯学习方法在仿真到现实转移中表现不佳且需要大量现实数据。本文介绍了AutoOdom，一种新的自回归本体里程计系统，通过创新的双阶段训练范式克服这些挑战。第一阶段利用大规模仿真数据学习腿足运动中复杂的非线性动态和快速变化的接触状态，第二阶段引入自回归增强机制利用有限的现实数据有效弥合仿真到现实的差距。关键创新在于我们的自回归训练方法，模型通过自身预测学习以增强对传感器噪声的鲁棒性，并在高度动态环境中提高鲁棒性。在Booster T1人形机器人上的全面实验验证表明，AutoOdom在所有评估指标上均显著优于现有方法，绝对轨迹误差改善57.2%，Umeyama对齐误差改善59.2%，相对姿态误差改善36.2%。广泛的消融研究提供了关于传感器模态选择和时间建模的关键见解，揭示了关于IMU加速度数据的反直觉发现，并验证了我们为挑战性运动场景设计的稳健本体里程计的系统性设计选择。
英文摘要：
Accurate proprioceptive odometry is fundamental for legged robot navigation in GPS-denied and visually degraded environments where conventional visual odometry systems fail. Current approaches face critical limitations: analytical filtering methods suffer from modeling uncertainties and cumulative drift, hybrid learning-filtering approaches remain constrained by their analytical components, while pure learning-based methods struggle with simulation-to-reality transfer and demand extensive real-world data collection. This paper introduces AutoOdom, a novel autoregressive proprioceptive odometry system that overcomes these challenges through an innovative two-stage training paradigm. Stage 1 employs large-scale simulation data to learn complex nonlinear dynamics and rapidly changing contact states inherent in legged locomotion, while Stage 2 introduces an autoregressive enhancement mechanism using limited real-world data to effectively bridge the sim-to-real gap. The key innovation lies in our autoregressive training approach, where the model learns from its own predictions to develop resilience against sensor noise and improve robustness in highly dynamic environments. Comprehensive experimental validation on the Booster T1 humanoid robot demonstrates that AutoOdom significantly outperforms state-of-the-art methods across all evaluation metrics, achieving 57.2% improvement in absolute trajectory error, 59.2% improvement in Umeyama-aligned error, and 36.2% improvement in relative pose error compared to the Legolas baseline. Extensive ablation studies provide critical insights into sensor modality selection and temporal modeling, revealing counterintuitive findings about IMU acceleration data and validating our systematic design choices for robust proprioceptive odometry in challenging locomotion scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
