高效鲁棒多传感器辅助惯性导航系统

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Woosik Lee, Patrick Geneva, Chuchu Chen, Guoquan Huang
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引用次数: 0

摘要

多模态测量的鲁棒多传感器融合,如惯性测量单元(imu)、车轮编码器、相机、激光雷达和GPS,由于其固有的能力,可以提高对传感器故障和测量异常值的恢复能力,从而实现鲁棒自治,因此具有巨大的潜力。据我们所知,这项研究是第一个开发一致的紧密耦合多传感器辅助惯性导航系统(MINS)的研究之一,该系统能够通过解决计算复杂性、传感器异步性和传感器内校准等特殊挑战,在有效的滤波框架中融合最常见的导航传感器。特别是,我们提出了一种一致的高阶流形插值方案,以实现高效的异步传感器融合和状态管理策略(即动态克隆)。本文提出的动态克隆利用运动诱导信息自适应选择插值顺序,以控制计算复杂度,同时最小化轨迹表示误差。我们对所有机载传感器进行在线内在和外在(时空)校准,以补偿先前校准不良和/或随时间变化的退化校准。此外,我们开发了一种初始化方法,仅使用IMU和车轮编码器的本体感受测量,而不是外部感受传感器,该方法受环境影响较小,在高动态场景下更健壮。我们在模拟和大规模具有挑战性的现实世界数据集中广泛验证了所提出的MINS,在定位精度,一致性和计算效率方面优于现有的最先进的方法。为了社区的利益,我们还开源了我们的算法、模拟器和评估工具箱:https://github.com/rpng/mins。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MINS: Efficient and Robust Multisensor-Aided Inertial Navigation System

MINS: Efficient and Robust Multisensor-Aided Inertial Navigation System

Robust multisensor fusion of multi-modal measurements such as inertial measurement units (IMUs), wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling robust autonomy. To the best of our knowledge, this study is among the first to develop a consistent tightly-coupled Multisensor-aided Inertial Navigation System (MINS) that is capable of fusing the most common navigation sensors in an efficient filtering framework, by addressing the particular challenges of computational complexity, sensor asynchronicity, and intra-sensor calibration. In particular, we propose a consistent high-order on-manifold interpolation scheme to enable an efficient asynchronous sensor fusion and state management strategy (i.e., dynamic cloning). The proposed dynamic cloning leverages motion-induced information to adaptively select interpolation orders to control computational complexity while minimizing trajectory representation errors. We perform online intrinsic and extrinsic (spatiotemporal) calibration of all onboard sensors to compensate for poor prior calibration and/or degraded calibration varying over time. Additionally, we develop an initialization method with only proprioceptive measurements of IMU and wheel encoders, instead of exteroceptive sensors, which is shown to be less affected by the environment and more robust in highly dynamic scenarios. We extensively validate the proposed MINS in simulations and large-scale challenging real-world datasets, outperforming the existing state-of-the-art methods, in terms of localization accuracy, consistency, and computation efficiency. We have also open-sourced our algorithm, simulator, and evaluation toolbox for the benefit of the community: https://github.com/rpng/mins.

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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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