多IMU融合算法：轻量级高精度定位方案

需积分: 50 131 浏览量更新于2024-08-05 3 收藏 1.59MB PDF 举报

"本文提出了一种轻量级且精确的多IMU（惯性测量单元）定位算法，通过融合多个IMU以及外部感知传感器的信息来提高定位精度。IMU是一种低成本的运动传感器，能提供旋转速度和重力补偿的线性加速度测量，广泛应用于现代定位系统。尽管单个IMU的定位在各种场景下表现出可接受的准确性和鲁棒性，但通过使用多个IMU可以进一步提升整体性能。为此，该算法旨在融合来自多个IMU及外部传感器的测量数据，以显著提升性能，同时不增加计算负担。为了实现这一目标，首先对多个IMU的测量结果进行概率映射，然后采用一种融合策略来处理这些信息，确保定位的准确性和实时性。此外，该算法还考虑了传感器误差校正和数据关联问题，以减小误差积累并提高长期稳定性。实验结果表明，与仅使用单个IMU的方案相比，提出的多IMU融合算法在定位精度和可靠性方面有显著优势，尤其适用于需要高精度定位的复杂环境和动态场景。" 这篇论文的核心知识点包括： 1. 多IMU融合：传统的惯性导航系统通常依赖于单一的IMU，而本文提出的方法利用多个IMU的互补性，通过融合不同IMU的数据，提高整体定位的准确性和鲁棒性。 2. 概率映射：在融合多个IMU数据的过程中，采用了概率方法对测量值进行处理，这有助于处理传感器之间的不确定性，并为融合过程提供了统计基础。 3. 外部感知传感器融合：除了IMU，该算法还结合了其他外部感知传感器的数据，如GPS、雷达或视觉传感器，以增强定位性能，特别是在GPS信号弱或者室内环境中。 4. 误差校正和数据关联：为了减少由于传感器误差和动态环境变化导致的定位漂移，该算法包含了误差校正机制，并解决了不同传感器数据之间的关联问题。 5. 轻量级设计：尽管增加了融合的复杂性，但算法设计时考虑了计算效率，确保在不增加过多计算负载的情况下，实现更优的定位性能。 6. 性能提升：通过实验验证，多IMU融合算法在定位精度和实时性上优于单IMU系统，特别适用于对定位精度要求高的应用，如自动驾驶、无人机导航等。这种多IMU融合的算法不仅深化了我们对惯性导航系统理解，也为实际应用中的高精度定位提供了新的解决方案。

IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JANUARY, 2020 1

A Lightweight and Accurate Localization Algorithm

Using Multiple Inertial Measurement Units

Ming Zhang, Xiangyu Xu, Yiming Chen, and Mingyang Li

Abstract—This paper proposes a novel inertial-aided local-

ization approach by fusing information from multiple inertial

measurement units (IMUs) and exteroceptive sensors. IMU is a

low-cost motion sensor which provides measurements on angular

velocity and gravity compensated linear acceleration of a moving

platform, and widely used in modern localization systems. To

date, most existing inertial-aided localization methods exploit

only one single IMU. While the single-IMU localization yields

acceptable accuracy and robustness for different use cases, the

overall performance can be further improved by using multiple

IMUs. To this end, we propose a lightweight and accurate

algorithm for fusing measurements from multiple IMUs and

exteroceptive sensors, which is able to obtain noticeable per-

formance gain without incurring additional computational cost.

To achieve this, we ﬁrst probabilistically map measurements

from all IMUs onto a virtual IMU. This step is performed

by stochastic estimation with least-square estimators and prob-

abilistic marginalization of inter-IMU rotational accelerations.

Subsequently, the propagation model for both state and error

state of the virtual IMU is also derived, which enables the

use of the classical ﬁlter-based or optimization-based sensor

fusion algorithms for localization. Finally, results from both

simulation and real-world tests are provided, which demonstrate

that the proposed algorithm outperforms competing algorithms

by noticeable margins.

Index Terms—Sensor Fusion; Localization; SLAM.

I. INTRODUCTION

N recent years, commercial products which exploit inertial

measurement units (IMUs) have been under fast develop-

ment. This popular motion sensor can be found in robotics,

personal electronic devices, wearable devices, and so on [1].

On one hand, the maturity of MEMS manufacturing process

signiﬁcantly reduces the size, price, and power consumption

of the IMU hardware. On the other hand, signiﬁcant progress

has also been made in both algorithm and software design

for IMUs, ranging from sensor characterization and calibra-

tion [2]–[5], measurement integration [6]–[8], sensor fusion

[9]–[13], and so on.

In this work, we focus on the inertial-aided localization,

which is to estimate the 6D poses (3D position and 3D

orientation) of a moving platform. Since localization with

only IMU inevitably suffers from pose drift, measurements

from other sensors (i.e. aiding), e.g., RGB cameras, depth

Manuscript received: September, 10, 2019; Revised December, 17, 2019;

Accepted January, 13, 2020.

This paper was recommended for publication by Editor Eric Marchand upon

evaluation of the Associate Editor and Reviewers’ comments.

The authors are with Alibaba Group, Hangzhou,

China. {mingzhang, xiangyuxu, yimingchen,

mingyangli}@alibaba-inc.com.

Digital Object Identiﬁer (DOI): see top of this page.

Fig. 1. The IMU array board used in this work, which contains nine ST

LSM6DSOX IMUs marked by red rectangles and a processor interface to

connect cameras. The IMUs are synchronized by an embedded processor.

cameras, or LiDARs (Light Detection And Ranging sensors),

are typically used in combination with IMUs to provide long-

term performance guarantees [12], [14], [15]. To perform

accurate pose estimation, the majority of existing works use

measurements from IMU for pose prediction, which is fol-

lowed by probabilistic reﬁnement using measurements from

other sensors [7]–[12].

To date, most algorithms on inertial-aided localization are

designed based on a single IMU [7]–[13]. Although these

algorithms are successfully deployed in different applications,

using additional IMU sensors creates new possibilities for

further improving the system accuracy and robustness. Com-

pared to other popular sensors for localization (e.g., cameras

or LiDARs), IMUs especially the off-the-shelf MEMS ones

are priced only hundredths or thousandths, and of smaller

size as well as lower power consumption. In addition, as a

reliable proprioceptive sensor, IMU also poses less restrictions

on operating environments and hardware conﬁgurations (in

contrast, e.g. stereo cameras require enough spatial baseline

to achieve performance gain [16], [17], which might not be

feasible on various applications including mobile devices).

Most existing methods on using multiple IMUs focus on

processing IMU measurements only or integration with global

navigation satellite systems (GNSS) [10], [18], [19]. Fusing

measurements from multiple IMUs with exteroceptive sensors

for localization is a less-explored topic. To the best of the

authors’ knowledge, the only work in recent years in this

domain is [20], which proposed an approach for vision-aided

inertial navigation using measurements from multiple IMUs.

However, the proposed algorithm is of signiﬁcantly increased

arXiv:1909.04869v2 [cs.RO] 17 Jan 2020

下载后可阅读完整内容，剩余7页未读，立即下载

Pasding

粉丝: 1
资源: 14

多IMU融合算法：轻量级高精度定位方案

Multi-Sensor Fusion (GNSS, IMU, Camera) 多源多传感器融合定位 GPS/INS组合导航 PPP/INS紧组合-C/C++开发

imu_gps_localization代码注释版.zip

simulink中的UWB和6轴imu融合算法程序

一种多imu融合的算法,通过将多个imu虚拟成一个imu实现融合。

gps imu融合定位算法

GPS和imu融合导航算法

gps-imu融合定位仿真,matlab

基于uwb和imu融合的三维空间定位算法matlab仿真

用matlab写一个imu解算算法

轮式里程计和imu融合

最新资源