Paving the way with machine learning for seamless indoor–outdoor positioning: A survey

Mallik, Manjarini; Panja, Ayan Kumar; Chowdhury, Chandreyee

doi:10.1016/j.inffus.2023.01.023

Cited by 22 publications

(10 citation statements)

References 77 publications

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“…The proposed solution operates independently of any deployed infrastructure, yet it is delivered through cost-effective equipment, intending to serve a wide number of users. This dual combination may enable the adoption of the proposed solution by other groups that might need robust and continuously seamless indoor/outdoor positioning, such as elderly and differently-abled people [1], wildlife [45], or warehouse assets [21]. Moreover, the proposed solution holds potential for applications in autonomous car and robot navigation; various automotive services such as vehicle accident detection; smart parking systems; and the estimation of free slots, real-time car parking monitoring, and automatic billing [21].…”

Section: Discussionmentioning

confidence: 99%

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Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

Angelats,

Gorreja,

Espín-López

et al. 2024

IJGI

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The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m.

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Section: Discussionmentioning

confidence: 99%

“…However, further progress is still necessary. One of the most relevant challenges is achieving seamless indoor-outdoor positioning on a global scale [1]. This requires a single positioning device capable of providing a reliable solution in any environment, whether it be outdoors or indoors.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

Angelats,

Gorreja,

Espín-López

et al. 2024

IJGI

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“…By leveraging machine learning algorithms and computer vision techniques, the wheelchair can differentiate between static and dynamic obstacles, determine their size and distance, and make informed decisions to navigate around them. This capability significantly reduces the risk of collisions and accidents, promoting user safety and confidence (Mallik et al, 2023). Furthermore, autonomous navigation and obstacle avoidance in smart robotic wheelchairs have a positive impact on mental well-being.…”

Section: Importance Of Proposed Studymentioning

confidence: 99%

Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

Sahoo,

Choudhury

2024

J. Decis. Anal. Int. Comp.

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This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.

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“…Machine learning includes random forests (RF) [13], decision trees (DT) [14], and other methods, these methods need to manually extract UWB CIR characteristics and establish the relationship between signal features and LOS/NLOS propagation through supervised learning. In Reference [15], an NLOS identification method with fuzzy credibility-based support vector machines (FC-SVM) and dynamic threshold comparison (DTC) is proposed, this is done in two steps, starting with a coarse-grained NLOS classification using the DTC approach, then moving on to a fine-grained result using FC-SVM.…”

Section: Related Work a Nlos Identificationmentioning

confidence: 99%

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Kong

2023

IEEE Access

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Ultra-wideband (UWB) is regarded as the technology with the most potential for precise indoor location due to its centimeter-level ranging capabilities, good time resolution, and low power consumption. However, Because of the presence of non-line-of-sight (NLOS) error, the accuracy of UWB localization deteriorates significantly in harsh and volatile indoor conditions. Therefore, identifying NLOS conditions is crucial to enhancing the accuracy of UWB location. This paper proposes a convolutional neural network (CNN) classification method based on an improved Dung Beetle Optimizer (DBO). Firstly, based on the standard DBO, the Circle chaotic mapping, non-uniform Gaussian variational strategy, and multi-stage perturbation strategy are used to optimize the exploration capability and enhance the performance of original DBO method, the superiority-seeking ability of IDBO is demonstrated by testing 23 benchmark functions. In addition, based on the IDBO algorithm, we propose an IDBO-CNN classification model, with the help of IDBO, the accuracy of NLOS identification is improved by adjusting the hyperparameters of CNN to be closer to the optimal solution. Experiments conducted on the open-source dataset demonstrate that IDBO-CNN is capable of achieve the desired effect. In comparison to the conventional CNN approach, the F1-score achieved by IDBO-CNN is enhanced by 3.31%, which demonstrates that IDBO-CNN has superior identification accuracy.

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Paving the way with machine learning for seamless indoor–outdoor positioning: A survey

Cited by 22 publications

References 77 publications

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

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