UNILS: Unconstrained indoors localization scheme based on cooperative smartphones networking with onboard inertial, Bluetooth and GNSS devices

Maghdid, Halgurd S.; Al-Sherbaz, Ali; Al-Jawad, Naseer; Lami, Ihsan Alshahib

doi:10.1109/plans.2016.7479692

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…Besides, the lack of scale information of the real world also limits the application of the monocular SLAM system in robot navigation. As a sensor that can measure its angular velocity and acceleration, IMU has long been used to assist systems to achieve localization [17], [18], [19], and it has complementary sensing capabilities with the camera. This visual and inertial fusion system is called VIO(Visual Inertial Odometry).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Visual-Inertial Localization Using Semantic Segmentation Towards Dynamic Environments

2020

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Simultaneous localization and mapping(SLAM), focusing on addressing the joint estimation problem of self-localization and scene mapping, has been widely used in many applications such as mobile robot, drone, and augmented reality(AR). However, traditional state-of-the-art SLAM approaches are typically designed under the static-world assumption and prone to be degraded by moving objects when running in dynamic scenes. This paper presents a novel semantic visual-inertial SLAM system for dynamic environments that, building on VINS-Mono, performs real-time trajectory estimation by utilizing the pixelwise results of semantic segmentation. We integrate the feature tracking and extraction framework into the front-end of the SLAM system, which could make full use of the time waiting for the completion of the semantic segmentation module, to effectively track the feature points on subsequent images from the camera. In this way, the system can track feature points stably even in high-speed movement. We also construct the dynamic feature detection module that combines the pixel-wise semantic segmentation results and the multi-view geometric constraints to exclude dynamic feature points. We evaluate our system in public datasets, including dynamic indoor scenes and outdoor scenes. Several experiments demonstrate that our system could achieve higher localization accuracy and robustness than state-of-the-art SLAM systems in challenging environments.INDEX TERMS Simultaneous localization and mapping, dynamic environment, semantic, visual-inertial system.

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Section: Related Workmentioning

confidence: 99%

Real-Time Visual-Inertial Localization Using Semantic Segmentation Towards Dynamic Environments

2020

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“…To overcome these concerns, including the lack of RSSI data samples, simulators can be considered as alternatives to real environments. The common simulators that are used in the positioning include the objective modular network testbed in C++ (OMNeT++) ( Irshad et al, 2021 ), Opnet ( Maghdid et al, 2016a ), and NS3 ( Zhao et al, 2021 ). In this work, we collected data using OMNeT++, a free simulator that is made available for academic use only.…”

Section: Introductionmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

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The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

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Evaluation Models for the Nearest Closer Routing Protocol in Wireless Sensor Networks

Cao

Liu

et al. 2018

IEEE Access

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UNILS: Unconstrained indoors localization scheme based on cooperative smartphones networking with onboard inertial, Bluetooth and GNSS devices

Cited by 11 publications

References 9 publications

Real-Time Visual-Inertial Localization Using Semantic Segmentation Towards Dynamic Environments

Real-Time Visual-Inertial Localization Using Semantic Segmentation Towards Dynamic Environments

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Evaluation Models for the Nearest Closer Routing Protocol in Wireless Sensor Networks

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