Integration of WiFi, Laser, and Map for Robot Indoor Localization

Zhaozheng HU; Jiahui LIU; Gang HUANG; Qianwen TAO

doi:10.11999/JEIT200671

Volume 43 Issue 8

Aug. 2021

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Zhaozheng HU, Jiahui LIU, Gang HUANG, Qianwen TAO. Integration of WiFi, Laser, and Map for Robot Indoor Localization[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2308-2316. doi: 10.11999/JEIT200671

Citation:

Zhaozheng HU, Jiahui LIU, Gang HUANG, Qianwen TAO. Integration of WiFi, Laser, and Map for Robot Indoor Localization[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2308-2316. doi: 10.11999/JEIT200671

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Integration of WiFi, Laser, and Map for Robot Indoor Localization

doi: 10.11999/JEIT200671

Zhaozheng HU^{1, 2
,
,},
Jiahui LIU^{1, 2},
Gang HUANG²,
Qianwen TAO²

1.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

Funds: The National Key R&D Program of China(2018YFB1600801), The National Natural Science Foundation of China(U1764262), The Funds of Wuhan Science and Technology Bureau(2020010601012165, 2020010602011973, 2020010602012003)

Received Date: 2020-08-04
Rev Recd Date: 2021-01-22

Available Online: 2021-01-29

Publish Date: 2021-08-10

Abstract

Abstract

WiFi-based localization methods suffer from multipath problem in indoor environments, which leads to poor accuracy. Light Detection And Ranging(LiDAR)-based localization methods can have good accuracy. However, they are not feasible in simple and repetitive scenarios as it is difficult for scene feature extraction and matching. Therefore, a novel localization method to fuse WiFi, LiDAR and Map by integrating them into a Kalman filter framework is proposed. In this framework, the state of the filter is defined as the current and historical position sequence of the robot. The observation consists of two parts. The first is the WiFi fingerprint localization results based on the proposed distance-weighted WiFi fingerprint matching method on multi-loop segmentation map; The second part comes from the high-precision relative localization results (such as lateral localization) by LiDAR in a single repeated scene. By utilizing the priori reference position in the scene map, such lateral positioning result can be integrated with the map to formulate linear constraints on the robot position. Finally, the Kalman filter is applied to accurate localization of the robot. The proposed algorithm is verified in two scenarios, where 2D and 3D LiDAR are applied. Experimental results show the average localization error of the proposed algorithm can be reduced by 70%～80%, which demonstrate that proposed method can improve the localization accuracy and stability.
- Map-based localization,
- Robot,
- Kalman filter,
- Light Detection And Ranging(LiDAR),
- WiFi fingerprint

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References(15)

References

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