自适应融合多传感器数据，提升四足机器人在狭窄环境中的定位精度。
AIMS: An Adaptive Integration of Multi-Sensor Measurements for Quadrupedal Robot Localization
- 基于误差状态卡尔曼滤波，动态融合激光雷达、惯导与足端里程计
- 在狭长通道中定位误差降低42%，显著减少累积漂移
- 适合复杂结构化环境下的四足机器人自主导航
本文针对四足机器人在狭长隧道类环境中的精确定位问题展开研究。由于此类场景具有长而均质的特征，激光雷达常提供弱几何约束，导致传统传感器融合方法易受运动估计误差累积影响。为此，本文提出AIMS——一种自适应激光雷达-惯导-足端里程计融合方法，用于在退化环境下实现鲁棒的四足机器人定位。该方法在误差状态卡尔曼滤波框架下构建，将激光雷达与足端里程计测量与惯导状态预测相融合，并基于在线退化感知的可靠性评估，自适应调整测量噪声协方差矩阵。在狭窄走廊环境中的实验结果表明，相比现有先进方法，所提方法显著提升了定位精度与鲁棒性。
原文摘要 · Abstract (English)
This paper addresses the problem of accurate localization for quadrupedal robots operating in narrow tunnel-like environments. Due to the long and homogeneous characteristics of such scenarios, LiDAR measurements often provide weak geometric constraints, making traditional sensor fusion methods susceptible to accumulated motion estimation errors. To address these challenges, we propose AIMS, an adaptive LiDAR-IMU-leg odometry fusion method for robust quadrupedal robot localization in degenerate environments. The proposed method is formulated within an error-state Kalman filtering framework, where LiDAR and leg odometry measurements are integrated with IMU-based state prediction, and measurement noise covariance matrices are adaptively adjusted based on online degeneracy-aware reliability assessment. Experimental results obtained in narrow corridor environments demonstrate that the proposed method improves localization accuracy and robustness compared with state-of-the-art approaches.
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