MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots

被引：0

作者：

Nistico, Ylenia ^{[1
,2
]}

Soares, Joao Carlos Virgolino ^{[1
]}

Amatucci, Lorenzo ^{[1
,2
]}

Fink, Geoff ^{[1
,3
]}

Semini, Claudio ^{[1
]}

机构：

[1] Ist Italiano Tecnol IIT, Dynam Legged Syst DLS, I-16163 Genoa, Italy

[2] Univ Genoa, Dipartimento Informat Bioingn Robot & Ingn Sistemi, I-16126 Genoa, Italy

[3] Thompson Rivers Univ, Dept Engn, Kamloops, BC V2C 0C8, Canada

来源：

IEEE ROBOTICS AND AUTOMATION LETTERS | 2025年 / 10卷 / 05期

关键词：

Robots; Sensors; Robot sensing systems; Legged locomotion; Odometry; Cameras; Laser radar; Robot vision systems; Robot kinematics; Quadrupedal robots; State estimation; localization; sensor fusion; quadruped robots;

D O I：

10.1109/LRA.2025.3553047

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

This letter 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 (Fink et al. 2020). 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 (Camurri et al. 2020) and VILENS (Wisth et al. 2022) showed 67.6% and 26.7% reductions in translational errors, respectively. Additionally, MUSE outperformed DLIO (Chen et al. 2023), a LiDAR-inertial odometry system in rotational errors and frequency, while the proprioceptive version of MUSE (P-MUSE) outperformed TSIF [Bloesch et al. 2018], with a 45.9% reduction in absolute trajectory error (ATE).

引用

页码：4620 / 4627

页数：8

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