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Sensors (Basel). 2018 Oct 22;18(10). pii: E3581. doi: 10.3390/s18103581.

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.

Shi Y1,2,3, Zhang W4,5,6, Yao Z7,8,9, Li M10,11,12, Liang Z13,14,15, Cao Z16, Zhang H17, Huang Q18,19,20.

Author information

1
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. ylshi@bit.edu.cn.
2
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. ylshi@bit.edu.cn.
3
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. ylshi@bit.edu.cn.
4
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. zhwm@bit.edu.cn.
5
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. zhwm@bit.edu.cn.
6
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. zhwm@bit.edu.cn.
7
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. 2220170106@bit.edu.cn.
8
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. 2220170106@bit.edu.cn.
9
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. 2220170106@bit.edu.cn.
10
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. limingzhu@bit.edu.cn.
11
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. limingzhu@bit.edu.cn.
12
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. limingzhu@bit.edu.cn.
13
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. 2220170089@bit.edu.cn.
14
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. 2220170089@bit.edu.cn.
15
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. 2220170089@bit.edu.cn.
16
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. czz1410407667@163.com.
17
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. zzhanghua@163.com.
18
School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China. qhuang@bit.edu.cn.
19
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China. qhuang@bit.edu.cn.
20
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100080, China. qhuang@bit.edu.cn.

Abstract

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.

KEYWORDS:

HPFL; KNNBP; indoor localization; multi-sensor fusion; precise localization; rough localization

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