Indoor localization of vehicles using Deep Learning

Kumar, Anil; Schaufele, Bernd; Becker, Daniel; Sawade, Oliver; Radusch, Ilja

doi:10.1109/wowmom.2016.7523569

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…From Table 1, it can be observed that the average localization error, max error, and Std.Dev of the proposed method are all smaller than the results in methods (13) and (15). The proportion of error ł 1 m is greater than the results in methods (13) and (15). From the comparison of computation time, it is found that the computation time has not increased too much even when WiFi and visual localization are fused.…”

Section: Results Of Comparison Experimentsmentioning

confidence: 73%

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Huang

et al. 2019

Transportation Research Record: Journal of the Transportation R

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Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.

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Section: Results Of Comparison Experimentsmentioning

confidence: 73%

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Huang

et al. 2019

Transportation Research Record: Journal of the Transportation R

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“…A similar work in presented in [326], where Zhou et al employ an MLP structure to perform device-free indoor localization using CSI. Kumar et al use deep learning to address the problem of indoor vehicles localization [320]. They employ CNNs to analyze visual signal and localize vehicles in a car park.…”

Section: E Deep Learning Driven User Localizationmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,377

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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“…In Reference [ 35 ], a fingerprinting based indoor positioning uses a deep neural network to reduce the error in positioning. Similarly, in Reference [ 36 ], the author introduced a location-based car park system based on the conventional neural network. This system is used to localize and identify the car in the parking area.…”

Section: Related Workmentioning

confidence: 99%

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Jamil

Iqbal

Ahmad

et al. 2020

Sensors

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Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

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Indoor localization of vehicles using Deep Learning

Cited by 22 publications

References 17 publications

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Deep Learning in Mobile and Wireless Networking: A Survey

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

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