Virtual 3D city model as a priori information source for vehicle localization system

Dawood, Maya; Cappelle, Cindy; Najjar, Maan E. El; Khalil, Mohamad; Hassan, Bachar El; Pomorski, Denis; Peng, Jing

doi:10.1016/j.trc.2015.12.003

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…For example, a low‐resolution 1‐m LIDAR model was used for NLOS and multipath mapping and a high LoD 3D city model was used for representing the environment in Virtual Reality Modeling Language (VRML) format . 3D building models have also been combined with terrestrial photogrammetry to produce highly detailed architectural models for aiding vehicle localization—matching real‐world with simulated images to constrain the position solution . City models are commonly stored using a boundary‐representation approach, where each face (wall, floor, and roof) of a building is described separately and a collection of faces is grouped to represent the building.…”

Section: Effect Of Mapping Qualitymentioning

confidence: 99%

“…25 3D building models have also been combined with terrestrial photogrammetry to produce highly detailed architectural models for aiding vehicle localization-matching real-world with simulated images to constrain the position solution. 26 City models are commonly stored using a boundary-representation approach, where each face (wall, floor, and roof) of a building is described separately and a collection of faces is grouped to represent the building. Each face is typically represented as a set of triangles.…”

Section: Effect Of Mapping Qualitymentioning

confidence: 99%

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Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

et al. 2019

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A full performance assessment of 3D‐mapping‐aided (3DMA) Global Navigation Satellite System (GNSS) in dense urban areas is presented. This second part of a two‐part paper focusses on the effects of the surrounding environment and 3D‐mapping, based on data collected in London using a u‐blox EVK M8T GNSS receiver. Conventional GNSS, shadow matching, 3DMA ranging, and integrated 3DMA GNSS all perform best when the proportion of directly visible sky is high, the building height to street width ratio is low, and the average building height is below 20 m. 3DMA GNSS methods demonstrate maximum benefit at sky visibilities of 15% to 35%. All methods exhibit poorer accuracy in environments dominated by glass and steel buildings. Temporary features, such as large buses and lorries, also degrade 3DMA accuracy. Using full 3D city models gives significantly higher accuracy than simple block models, and missing buildings lead to larger positioning errors. Further enhancements to the 3DMA GNSS algorithms are recommended.

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Section: Effect Of Mapping Qualitymentioning

confidence: 99%

Section: Effect Of Mapping Qualitymentioning

confidence: 99%

Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

et al. 2019

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“…However, due to the intrinsic nature of data acquisition and recording technology, it is not easy to capture a large dataset with complete and accurate trajectory information. For example, in the well-known Next Generation SIMulation program (NGSIM), the mistracking of data is very likely to occur when deriving trajectory data from video images [1] and in the probe vehicle case, vehicle location data based on GPS is sometimes untracked because of signal loss or interference [2]. As a consequence, inaccurate and missing values in the real-world vehicle trajectory data are very common, which undermines its application in traffic modelling and simulation.…”

Section: Introductionmentioning

confidence: 99%

Car‐following trajectory data imputation with adversarial convolutional neural network

Zhao

Zhang

Wang

et al. 2022

IET Intelligent Trans Sys

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Missing values in the vehicle trajectory data undermine its application in traffic modelling and simulation. Traditional methods for missing data imputation rely on neighbor points of the missing/distorted data point and consequently can hardly handle trajectory with consecutive data loss. To fill in this research gap, external data with latent information should be considered to enhance the imputation. Hence, the primary objective of this study is to find an effective approach to impute consecutive missing data in the vehicle trajectory making use of leading vehicle trajectory. We proposed a novel imputation adversarial convolutional neural network (IACNN) by extending the CNN model with a multi-objective loss function and adversarial learning framework for vehicle trajectory data imputation. Performance of the model is evaluated on the commonly used trajectory dataset NGSIM with comparison to other baseline models. It turns out that the proposed IACNN outperforms other baseline models in most data loss scenarios, especially with consecutive data loss.

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“…The second category is based on a combination of multi-source information sensors. Widely used sensor technologies (Dao et al, 2014;Dawood et al, 2016), such as obliquity sensors, ranging non-contact sensors and ultrasonic and electromagnetic sensors in power systems provide a new approach for monitoring of robot posture and manipulator location control. This approach has been widely and successfully applied but again increases the cost of robot hardware in most situations.…”

Section: Introductionmentioning

confidence: 99%

Autonomous location control of a robot manipulator for live maintenance of high-voltage transmission lines

Jiang

Fan

et al. 2017

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Purpose This paper aims to develop a robot for tightening charged bolt to solve the shortcomings of high labor intensity, low efficiency, high risk and poor reliability in artificially tightening drainage board bolt of strain clamp for high voltage transmission line. Realizing bolt-nut capture and location by manipulator is a critical process to complete the whole working task. To solve such key technology, an autonomous location control method for N-joint robot manipulator based on kinematics was proposed. Design/methodology/approach Through D-H kinematics analysis under flexible working environment of transmission line, the autonomous location control of double manipulators can be abstracted as a nonlinear approximation problem based on joint inverse kinematics. In addition, regarding the complex coupling relationship among different joint angles and the complex decoupling process which leads to the non-uniqueness of inverse solution, an improved backpropagation (BP) network was proposed based on the combination of dynamic adaptive adjustment of learning rate and variable momentum factor, so that the inverse kinematics of manipulator can be solved and the optimization evaluation mechanism of inverse solution can be presented. The proposed autonomous location control method is of adaptability to flexible environment and structural parameters of different drainage boards. The simulation results verified the effectiveness of the proposed method. Compared with the other location control, this method can achieve faster location speed, higher precision and lower hardware cost. Finally, the field operation test further validated that such autonomous location control method was of strong engineering practicability. Findings The proposed autonomous location control method is adaptable to a flexible environment and to the structural parameters of different types of drainage board. Simulation results confirm the effectiveness of the proposed method, which, in comparison with other approaches to location control, can achieve faster location, higher precision and lower hardware cost. Finally, a field test further confirms the engineering practicability of the proposed autonomous location control method. Originality/value The proposed method can achieve faster location speed, higher precision which meet the requirement of real-time control relative to the standard BP algorithm. Moreover, it is of strong adaptability to flexible environment and structural parameters for different drainage board. Field operation experiment further validated the engineering practicability of the method.

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Virtual 3D city model as a priori information source for vehicle localization system

Cited by 9 publications

References 30 publications

Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

Car‐following trajectory data imputation with adversarial convolutional neural network

Autonomous location control of a robot manipulator for live maintenance of high-voltage transmission lines

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