MUSE实时融合多传感器数据，提升四足机器人在复杂地形下的定位精度。
MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots
- 融合IMU、编码器、相机和激光雷达数据，实现高精度状态估计。
- 在光滑不平地形上，平移误差比Pronto降低67.6%，旋转性能优于DLIO。
- 适合需要高鲁棒性实时定位的四足机器人研发与控制团队使用。
本文提出一种新型状态估计算法MUSE（MUlti-sensor State Estimator），旨在提升四足机器人导航中的状态估计精度与实时性能。该方法基于先前工作[1]，整合了惯性测量单元（IMU）、编码器、摄像头和激光雷达等机载传感器数据，在湿滑等复杂场景下仍能提供可靠的姿态与运动估计。我们在Unitree Aliengo机器人上验证了MUSE，成功在困难地形中闭环控制步态。对比Pronto[2]和VILENS[3]，MUSE在平移误差上分别降低了67.6%和26.7%；相较于LiDAR-惯性里程计系统DLIO[4]，在旋转误差与更新频率上表现更优；其本体感知版本P-MUSE在绝对轨迹误差（ATE）上较TSIF[5]降低45.9%。
原文摘要 · Abstract (English)
This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in [1]. It integrates data from a range of onboard sensors, including IMUs, encoders, cameras, and LiDARs, to deliver a comprehensive and reliable estimation of the robot's pose and motion, even in slippery scenarios. We tested MUSE on a Unitree Aliengo robot, successfully closing the locomotion control loop in difficult scenarios, including slippery and uneven terrain. Benchmarking against Pronto [2] and VILENS [3] showed 67.6% and 26.7% reductions in translational errors, respectively. Additionally, MUSE outperformed DLIO [4], a LiDAR-inertial odometry system in rotational errors and frequency, while the proprioceptive version of MUSE (P-MUSE) outperformed TSIF [5], with a 45.9% reduction in absolute trajectory error (ATE).
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