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18 pages, 1177 KiB  
Article
Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
by Yongliang Shi, Weimin Zhang, Zhuo Yao, Mingzhu Li, Zhenshuo Liang, Zhongzhong Cao, Hua Zhang and Qiang Huang
Sensors 2018, 18(10), 3581; https://doi.org/10.3390/s18103581 - 22 Oct 2018
Cited by 29 | Viewed by 7153
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
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