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基于地图的室内行人导航辅助粒子滤波方法

Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter.

作者信息

Yu Chunyang, El-Sheimy Naser, Lan Haiyu, Liu Zhenbo

机构信息

College of Automation, Haibin Engineering University, Harbin 150001, China.

Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

出版信息

Micromachines (Basel). 2017 Jul 19;8(7):225. doi: 10.3390/mi8070225.

DOI:10.3390/mi8070225
PMID:30400415
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6189818/
Abstract

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.

摘要

在本研究中，通过使用辅助粒子滤波器（APF）将智能手机内置的微机电系统（MEMS）传感器与室内地图信息相结合，提出了一种基于非基础设施的低成本室内导航方法。设计了一种级联结构卡尔曼粒子滤波器算法，通过降低其更新频率和本研究中使用的粒子数量，来减轻计算负担并提高APF的估计速度。在较低层滤波器（卡尔曼滤波器）中，使用零速度更新和非完整约束来校正惯性导航导出解的误差。该设计的创新之处在于上层滤波器（粒子滤波器）的地图匹配和地图辅助方法相结合，以进一步约束导航解。所提出的导航方法简化了室内定位，使个人和团体用户都能使用，同时保证了系统的准确性。通过在各种实际场景中的实验，对所提出算法的可用性和准确性进行了测试和验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/98431f7b3f37/micromachines-08-00225-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/a8957ee1c921/micromachines-08-00225-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/c6b768a1ba95/micromachines-08-00225-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/98431f7b3f37/micromachines-08-00225-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/0def6988bd9b/micromachines-08-00225-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/aced2bba9dc5/micromachines-08-00225-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/b69dd5852858/micromachines-08-00225-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/5617b1be764b/micromachines-08-00225-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/8437349fa7d4/micromachines-08-00225-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/7399db74c80b/micromachines-08-00225-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/a8957ee1c921/micromachines-08-00225-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/c6b768a1ba95/micromachines-08-00225-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/96c99ac0c4fb/micromachines-08-00225-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/ca86e3c26cb6/micromachines-08-00225-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/9b12c8ebe82e/micromachines-08-00225-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/6189818/98431f7b3f37/micromachines-08-00225-g013.jpg

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