提出自回归式本体感知里程计，提升足式机器人在无视觉环境下的定位精度。
AutoOdom: Learning Auto-regressive Proprioceptive Odometry for Legged Locomotion
- 分两阶段训练：先用仿真数据学非线性动力学，再用少量真实数据优化
- 实测误差降低超50%，相较基线系统显著提升定位准确性
- 适合需高鲁棒性定位的足式机器人，尤其在无视觉场景中
准确的本体感知里程计对于足式机器人在无GPS和视觉退化的环境中导航至关重要。现有方法存在明显局限：解析滤波方法受建模不确定性与累积漂移影响，混合学习-滤波方法受限于解析组件，纯学习方法难以实现从仿真到现实的迁移且依赖大量真实数据。本文提出AutoOdom，一种新型自回归本体感知里程计系统，通过创新的两阶段训练范式克服上述挑战。第一阶段利用大规模仿真数据学习足式运动中的复杂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，使用有限真实数据有效弥合仿真与现实差距。核心创新在于自回归训练策略，使模型从自身预测中学习，增强对传感器噪声的鲁棒性，提升在高度动态环境中的稳定性。在Booster T1人形机器人上的全面实验表明，AutoOdom在所有评估指标上显著优于当前最优方法：绝对轨迹误差降低57.2%，Umeyama对齐误差减少59.2%，相对位姿误差下降36.2%（相比Legolas基线）。大量消融研究揭示了传感器模态选择与时间建模的关键作用，得出关于IMU加速度数据的反直觉发现，并验证了系统设计的合理性，为复杂运动场景下的鲁棒本体感知里程计提供支持。
原文摘要 · Abstract (English)
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.
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