Paper Info
Title
Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.
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.
Year
DOI
Venue
2018
10.3390/s18103581
SENSORS
Keywords
Field
DocType
indoor localization,multi-sensor fusion,rough localization,KNNBP,precise localization,HPFL
Real-time computing,Sensor fusion,Electronic engineering,Engineering,Robot,Location systems
Journal
Volume
Issue
ISSN
18
10.0
1424-8220
Citations 
PageRank 
References 
0
0.34
21
Authors
8
Name
Order
Citations
PageRank
Yongliang Shi112.32
Weimin Zhang27221.91
Zhuo Yao301.69
Mingzhu Li433.09
Zhenshuo Liang501.69
Zhongzhong Cao600.34
Hua Zhang7122.87
Qiang Huang826691.95