MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912297285320704 |
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| author | Nisticò, Ylenia Soares, João Carlos Virgolino Amatucci, Lorenzo Fink, Geoff Semini, Claudio |
| author_facet | Nisticò, Ylenia Soares, João Carlos Virgolino Amatucci, Lorenzo Fink, Geoff Semini, Claudio |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots Nisticò, Ylenia Soares, João Carlos Virgolino Amatucci, Lorenzo Fink, Geoff Semini, Claudio Robotics Signal Processing 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). |
| title | MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots |
| topic | Robotics Signal Processing |
| url | https://arxiv.org/abs/2503.12101 |