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.

翻译：本文针对四足机器人在狭窄隧道类环境中运行的精确定位问题展开研究。由于此类场景具有长而均匀的特性，激光雷达测量通常只能提供较弱的几何约束，使得传统传感器融合方法容易受到累积运动估计误差的影响。为应对这些挑战，我们提出AIMS，一种用于退化环境中四足机器人鲁棒定位的自适应激光雷达-IMU-腿部里程计融合方法。该方法在误差状态卡尔曼滤波框架内构建，其中激光雷达与腿部里程计测量结果与基于IMU的状态预测相融合，并且测量噪声协方差矩阵基于在线退化感知可靠性评估进行自适应调整。在狭窄走廊环境中获得的实验结果表明，与现有先进方法相比，所提方法显著提升了定位精度与鲁棒性。