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Open AccessArticle

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

1
GNSS Research Center, Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 949; https://doi.org/10.3390/rs12060949
Received: 12 February 2020 / Revised: 13 March 2020 / Accepted: 13 March 2020 / Published: 15 March 2020
Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct. View Full-Text
Keywords: cooperative ambiguity resolution; GNSS RTK; Inertial Navigation System; LiDAR scan-to-map matching; connected vehicles cooperative ambiguity resolution; GNSS RTK; Inertial Navigation System; LiDAR scan-to-map matching; connected vehicles
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MDPI and ACS Style

Qian, C.; Zhang, H.; Li, W.; Tang, J.; Liu, H.; Li, B. Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles. Remote Sens. 2020, 12, 949. https://doi.org/10.3390/rs12060949

AMA Style

Qian C, Zhang H, Li W, Tang J, Liu H, Li B. Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles. Remote Sensing. 2020; 12(6):949. https://doi.org/10.3390/rs12060949

Chicago/Turabian Style

Qian, Chuang; Zhang, Hongjuan; Li, Wenzhuo; Tang, Jian; Liu, Hui; Li, Bijun. 2020. "Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles" Remote Sens. 12, no. 6: 949. https://doi.org/10.3390/rs12060949

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