Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

Wen, Weisong; Bai, Xiwei; Zhang, Guohao; Chen, Shengdong; Yuan, Feng; Hsu, Li‐Ta

doi:10.1109/access.2020.3006210

Cited by 21 publications

(7 citation statements)

References 42 publications

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“…In order to verify the proposed hybrid model in this paper, a typical urban intersection was selected as the experiment scenario [40,41]. In this experiment, real-time vehicular compound positioning data were selected as the model input.…”

Section: Field Experiments and Analysismentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

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With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

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Section: Field Experiments and Analysismentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

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“…In order to detect the UAV in cooperative positioning scenarios better, the preset anchor boxes are no longer used, and they are recalculated by K-means algorithm. The distance function is shown in Equation (1).…”

Section: Object Detection With Yolov3mentioning

confidence: 99%

“…Robust and accurate positioning is significant for the autonomous systems with navigation requirements, such as intelligent transportation systems [1]. Traditional Global Navigation Satellite System (GNSS)-based navigation solution is generally degraded in challenged signal environments such as urban canyons when an unmanned ground vehicle (UGV) performs mission.…”

Section: Introductionmentioning

confidence: 99%

UGV‐UAV robust cooperative positioning algorithm with object detection

Wang

Lian

Tang

2021

IET Intelligent Trans Sys

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Traditional Global Navigation Satellite Systems (GNSS) experience their limitations in urban canyons. However, it is significant to improve the accuracy of positioning with the rapid development of smart cities. To solve this problem, a UGV-UAV robust cooperative positioning algorithm with object detection is proposed, which utilises an unmanned aerial vehicle (UAV) to assist an unmanned ground vehicle (UGV) to achieve accurate positioning. When the UAV is in the sky with a good reception of satellite signals, the UGV uses the YOLOv3 object detection method to detect the UAV in images captured by camera, and acquires visual measurements including angles and ranges of the ground camera relative to the UAV through the proposed monocular vision measuring with object detection (ODMVM) model. Then, in order to solve the problem that visual measurement is disturbed by the real world, a robust Kalman filter is introduced that integrates measurements from available GNSS, inertial measurement unit (IMU), monocular camera, and the position broadcast of cooperative UAV to obtain more robust and accurate position estimation. Experimental and simulation results show that the proposed cooperation positioning algorithm can improve the positioning accuracy by 73.63% compared with the traditional cooperation positioning algorithm in urban canyons.

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“…Wen et al. [23] proposed a multi‐agent collaborative integration (MCI) method utilising GNSS/camera/INS aided by inter‐ranging measurements. Two vehicle tests were performed in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

Kim

Lee

2021

IET Radar Sonar & Navi

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For improved positioning in urban canyons, this study proposes an efficient dual‐filter method integrating multi‐constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on‐board diagnostics (OBD) module. The proposed method consists of a position‐domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub‐filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi‐simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low‐cost all‐weather sensors. The RMSEs of the proposed Kalman–Hatch dual‐filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.

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Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

Cited by 21 publications

References 42 publications

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

UGV‐UAV robust cooperative positioning algorithm with object detection

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

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