An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver

Kang, Jeong Min; Kim, Han Sol; Park, Jin Bae; Choi, Yoon Ho

doi:10.3390/s18113836

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Thus we enhance the proposed multi-criteria algorithm with an HMM. The HMM in this work is an upgrade of the proposed HMM by [38] [39]. We fitted the HMM to the way selection problem.…”

Section: Selection Of the Correct Waymentioning

confidence: 99%

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End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most works presented in the literature, ego-localization is split into three parts: Road level-localization consisting in the road on which the vehicle travels, Lane level localization which is the lane on which the vehicle travels, and Ego lane level localization being the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with two main novelties. The first one is the proposition of a complete solution that tackles every part of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.

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“…Thus we enhance the proposed multi-criteria algorithm with an HMM. The HMM in this work is an upgrade of the proposed HMM by [38] [39]. We fitted the HMM to the way selection problem.…”

Section: Selection Of the Correct Waymentioning

confidence: 99%

“…However, the big difference lies in the probabilistic reasoning. Indeed, we use criteria that represent probability to model the observation probability, which is not the case in [38] [39], where arbitrary functions have been introduced leading to non-intuitive criteria.…”

Section: Selection Of the Correct Waymentioning

confidence: 99%

End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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show abstract

“…We further refined the reference map to achieve lane-level accuracy. Initially, as in a previous study, the driving azimuth is divided into east, west, south, and north [36]. Each link is stored separately from the median lane, which is a line indicated with safety signs, such as yellow solid lines and yellow dotted lines, or facilities [43].…”

Section: Description Of the Reference Mapmentioning

confidence: 99%

“…However, this method could not present the position error of each vehicle point. Therefore, a method suggesting the accuracy of each point that is based on an iterative closest point (ICP) algorithm utilizing only GPS trajectory and map information was proposed [36]. This method enabled calculation of the position errors via consideration of the information acquired by the high-end equipment as a ground truth.…”

Section: Introductionmentioning

confidence: 99%

Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map

Kang

Yoon

Kim

et al. 2020

Sensors

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Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

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WITHDRAWN: 3 Axis Gyro Accelerometer & Artificial Intelligence based Enhancement of GPS Accuracy

Kathpalia

Gulati

2022

Measurement: Sensors

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An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver

Cited by 5 publications

References 26 publications

End-to-End Probabilistic Ego-Vehicle Localization Framework

End-to-End Probabilistic Ego-Vehicle Localization Framework

Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map

WITHDRAWN: 3 Axis Gyro Accelerometer & Artificial Intelligence based Enhancement of GPS Accuracy

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