GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

Miura, Satoshi; Hsu, Li‐Ta; Chen, Feiyu; Kamijo, Shunsuke

doi:10.1109/tits.2015.2432122

Cited by 129 publications

(81 citation statements)

References 16 publications

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“…The detail of the 3D-GNSS method can be found in [7] and [8]. The previous developed 3D-GNSS is used to estimate the user position originally.…”

Section: A Methodology Of 3d-gnssmentioning

confidence: 99%

“…Recently, the use of three-dimension (3D) city building model as an aid to mitigate, exclude or even correct the multipath and non-line-of-sight (NLOS) effects becomes popular in GNSS research field [3]- [12]. In 2013, the research team of The University of Tokyo developed a particle filter based GNSS positioning method using a basic 3D city building to estimate the positioning result of commercial GNSS single frequency receiver [7], [8], [12]. We call it 3D-GNSS.…”

Section: Introductionmentioning

confidence: 99%

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Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

Hsu

Huang

et al. 2016

IEEE Sensors J.

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This paper focuses on the pedestrian navigation in highly urbanized area, where a current smartphone and a commercial global navigation satellite system (GNSS) receiver perform poorly because of the reflection and blockage of GNSS signal by buildings and foliage. A 3-D map-aided pedestrian positioning method is previously developed to mitigate and correct the multipath GNSS signal. However, it still suffers from the low availability due to the insufficient number of satellites. We develop a smartphone-based pedestrian dead reckoning (PDR) algorithm, which is carried in the pedestrian's trousers. This PDR is capable of not only providing continues solutions but also indicating the pedestrian motions. A closedloop Kalman filter with adaptive tuning is proposed to integrate the 3-D map-aided GNSS method with the smartphone-based PDR system. According to the experiment results, the proposed integration system can achieve ∼1.5-and 5.5-m of positioning errors in a middle-class and deep urban canyon, respectively.

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“…The detail of the 3D-GNSS method can be found in [7] and [8]. The previous developed 3D-GNSS is used to estimate the user position originally.…”

Section: A Methodology Of 3d-gnssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

Hsu

Huang

et al. 2016

IEEE Sensors J.

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“…The mission planning module may generate these reference signals according to road regulations, planned vehicle route, and flows of the lanes [15][16][17]. The path planning module also receives the shape, position, and velocity of the obstacles, the road structure, and the regulations from the perception module [18][19][20], and the vehicle states from the estimation module [21][22][23]. The goal of the path planning module is to plan a path following the commands of the mission planning module while meeting the road regulations, avoiding the obstacles, and having a stable vehicle dynamics.…”

Section: Overall Vehicle Systemmentioning

confidence: 99%

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Rasekhipour

Khajepour

Chen

et al. 2017

IEEE Trans. Intell. Transport. Syst.

508

194

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Abstract-Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures, and plans the path based on these potential functions. It does not however, include the vehicle dynamics in the path planning process. On the other hand, an optimal path planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints, and not with any arbitrary function. A model predictive path planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path planning system plans paths corresponding to their importance and priorities.Index Terms-Path Planning, Autonomous Vehicles, Road Vehicles, Model Predictive Control, Artificial Potential Field, Vehicle Dynamics and Control.

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“…Streets around the Hitotsubashi intersection are surrounded by tall buildings, trees, and traffic. A survey on the number of Line-of-Sight (LOS) GPS satellites in this area showed that, two-thirds of the time, less than four satellites were visible [58]. This shortage increases the positioning error to a few meters, which is unsuitable for precise mapping.…”

Section: Experimental Areamentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

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GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

Cited by 129 publications

References 16 publications

Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

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