MUSE：四足机器人的实时多传感器状态估计器

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI:10.1109/LRA.2025.3553047

Ylenia Nisticò;João Carlos Virgolino Soares;Lorenzo Amatucci;Geoff Fink;Claudio Semini

{"title":"MUSE：四足机器人的实时多传感器状态估计器","authors":"Ylenia Nisticò;João Carlos Virgolino Soares;Lorenzo Amatucci;Geoff Fink;Claudio Semini","doi":"10.1109/LRA.2025.3553047","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4620-4627"},"PeriodicalIF":4.6000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots\",\"authors\":\"Ylenia Nisticò;João Carlos Virgolino Soares;Lorenzo Amatucci;Geoff Fink;Claudio Semini\",\"doi\":\"10.1109/LRA.2025.3553047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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).\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":\"10 5\",\"pages\":\"4620-4627\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10933515/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10933515/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}

引用次数: 0

摘要

这封信介绍了一种创新的状态估计器 MUSE（MUlti-sensor State Estimator），旨在提高四足机器人导航中状态估计的准确性和实时性。所提出的状态估算器建立在我们之前的工作（Fink 等，2020 年）基础之上。它整合了一系列机载传感器（包括 IMU、编码器、摄像头和激光雷达）的数据，即使在湿滑的情况下，也能对机器人的姿态和运动进行全面可靠的估计。我们在 Unitree Aliengo 机器人上对 MUSE 进行了测试，在包括湿滑和不平坦地形在内的困难场景中成功地关闭了运动控制环。与 Pronto（Camurri 等人，2020 年）和 VILENS（Wisth 等人，2022 年）进行的基准测试显示，平移误差分别减少了 67.6% 和 26.7%。此外，MUSE 在旋转误差和频率方面的表现优于 DLIO（Chen 等人，2023 年），后者是一种激光雷达-惯性里程测量系统，而 MUSE 的本体感觉版本（P-MUSE）则优于 TSIF [Bloesch 等人，2018 年]，绝对轨迹误差（ATE）减少了 45.9%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.