DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones

Zhou, Baoding; Gu, Zhining; Wu, Peng; Yang, Chengjing; Liu, Xu; Li, Linchao; Li, Yan; Li, Qingquan

doi:10.1109/tvt.2022.3199507

Cited by 16 publications

(4 citation statements)

References 39 publications

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“…Xiangyu Wang et al [35] introduced an ensemble technique that merges various DNN models for training, utilizing pre-existing mixed data comprising RSS/ AOA and RSS/CSI. Baoding Zhou et al [36] suggeted a plan that utilizes the built-in sensors of smartphones, which include accelerometers, gyroscopes, magnetometers, and gravity sensors, and uses them for deep learning-based indoor localization of vehicles. However, the above algorithms require too much hardware support, resulting in a vast training volume and high cost.…”

Section: Related Workmentioning

confidence: 99%

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Zhou,

Yang,

Deng

et al. 2024

Eng. Res. Express

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With the popularization and application of WiFi technology, precise location in multi-layer indoor environments play an increasingly important role in current public places. However, there are still problems such as noise and multi-path effects caused by the propagation degradation of the Received Signal Strength Indicator (RSSI) in indoor scenarios and the shading of obstacles, which lead to a reduction in indoor location accuracy. In this paper, we propose a Vision Transformer indoor localization (VTIL) algorithm based on RSSI and Vision-Transformer (ViT). First, the RSSI fingerprint is normalized to the maximum and minimum values, and the Principal Component Analysis (PCA) method is used for feature extraction to reduce the influence of noise or unimportant features. Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our method reduces the average distance estimation error by 37.26% compared with the currently known indoor fingerprint localization methods.

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Section: Related Workmentioning

confidence: 99%

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Zhou,

Yang,

Deng

et al. 2024

Eng. Res. Express

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“…This is illustrated in Figure 2 (b). By producing more accurate [34] Pedestrian, Trolley LSTM SL location displacement RIDI [35] Pedestrian SVM, SVR SL velocity for inertial data calibration Cortes et al [36] Pedestrian ConvNet SL velocity to constrain system drifts Wagstaff et al [37] Pedestrian LSTM SL zero-velocity detection for ZUPT Chen et al [38] Pedestrian, Trolley LSTM TL location displacement AbolDeepIO [39] UAV LSTM SL location displacement RINS-W [40] Vehicle RNN SL zero-velocity dection for KF Feigl et al [41] Pedestrian LSTM SL walking velocity Wang et al [42] Pedestrian LSTM SL walking heading for ZUPT Yu et al [43] Pedestrian ConvNet SL adaptive zero-velocity detection TLIO [44] Pedestrian ConvNet SL 3D displacement and uncertainty for EKF LIONet [45] Pedestrian Dilated ConvNet SL lightweight inertial model RoNIN [46] Pedestrian LSTM, TCN SL velocity for inertial data calibration Brossard et al [47] Vehicle ConvNet SL co-variance noise for KF StepNet [48] Pedestrian ConvNet, LSTM SL dynamic step length for PDR Wang et al [49] Pedestrian ConvNet SL measurement noise for Kalman Filter ARPDR [50] Pedestrian TCN SL stride length and walking heading for PDR IDOL [51] Pedestrian LSTM SL device orientation and location PDRNet [52] Pedestrian ConvNet SL step length and heading for PDR Buchanan et al [53] Legged Robot ConvNet SL integrate location displacement with leg odometry Zhang et al [54] Vehicle, UAV RNN SL independent motion terms Gong et al [55] Pedestrian LSTM SL fusing inertial data from two devices NILoc [56] Pedestrian ConvNet SL inertial relocalization RIO [57] Pedestrian DNN UL rotation-equivariance as supervision signal Wang et al [58] Pedestrian DNN SL efficient and low-latent model TinyOdom [59] Pedestrian, Vehicle TCN+NAS SL deployment on resource-constrained device CTIN [60] Pedestrian Transformer SL velocity and trajectory prediction DeepVIP [61] Vehicle ConvNet, LSTM SL velocity and heading for car localization Bo et al…”

Section: Deep Learning Based Inertial Sensor Calibrationmentioning

confidence: 99%

Deep Learning for Inertial Positioning: A Survey

Chen¹

2023

Preprint

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Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensorbased positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learningbased techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

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“…In recent years, black-box modelling-based technologies have gained significant traction too. In the field of vehicle localization a numerous papers [24][25][26][27][28][29][30][31] have been published. Maybe even more attention is on smartphone-based pedestrian tracking [32][33][34][35][36].…”

Section: Knowledge-based Versus Black-box Modelingmentioning

confidence: 99%

Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis

Markó,

Horváth,

Szalay

et al. 2023

Machines

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In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth.

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DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones

Cited by 16 publications

References 39 publications

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Deep Learning for Inertial Positioning: A Survey

Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis

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