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24 pages, 7979 KiB  
Article
Vision-Based Hand Gesture Recognition Using a YOLOv8n Model for the Navigation of a Smart Wheelchair
by Thanh-Hai Nguyen, Ba-Viet Ngo and Thanh-Nghia Nguyen
Electronics 2025, 14(4), 734; https://doi.org/10.3390/electronics14040734 - 13 Feb 2025
Cited by 2 | Viewed by 2449
Abstract
Electric wheelchairs are the primary means of transportation that enable individuals with disabilities to move independently to their desired locations. This paper introduces a novel, low-cost smart wheelchair system designed to enhance the mobility of individuals with severe disabilities through hand gesture recognition. [...] Read more.
Electric wheelchairs are the primary means of transportation that enable individuals with disabilities to move independently to their desired locations. This paper introduces a novel, low-cost smart wheelchair system designed to enhance the mobility of individuals with severe disabilities through hand gesture recognition. Additionally, the system aims to support low-income individuals who previously lacked access to smart wheelchairs. Unlike existing methods that rely on expensive hardware or complex systems, the proposed system utilizes an affordable webcam and an Nvidia Jetson Nano embedded computer to process and recognize six distinct hand gestures—“Forward 1”, “Forward 2”, “Backward”, “Left”, “Right”, and “Stop”—to assist with wheelchair navigation. The system employs the “You Only Look Once version 8n” (YOLOv8n) model, which is well suited for low-spec embedded computers, trained on a self-collected hand gesture dataset containing 12,000 images. The pre-processing phase utilizes the MediaPipe library to generate landmark hand images, remove the background, and then extract the region of interest (ROI) of the hand gestures, significantly improving gesture recognition accuracy compared to previous methods that relied solely on hand images. Experimental results demonstrate impressive performance, achieving 99.3% gesture recognition accuracy and 93.8% overall movement accuracy in diverse indoor and outdoor environments. Furthermore, this paper presents a control circuit system that can be easily installed on any existing electric wheelchair. This approach offers a cost-effective, real-time solution that enhances the autonomy of individuals with severe disabilities in daily activities, laying the foundation for the development of affordable smart wheelchairs. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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22 pages, 4616 KiB  
Article
Automatic Generation of Guidance for Indoor Navigation at Metro Stations
by Jacek Bernard Marciniak and Bartosz Wiktorzak
Appl. Sci. 2024, 14(22), 10252; https://doi.org/10.3390/app142210252 - 7 Nov 2024
Cited by 3 | Viewed by 1338
Abstract
This article delves into the advancements in indoor navigation in metro stations and describes the development and implementation of algorithms for the automatic generation of navigation guidance. The LIFT project at the Warsaw University of Technology serves as a practical example, showcasing a [...] Read more.
This article delves into the advancements in indoor navigation in metro stations and describes the development and implementation of algorithms for the automatic generation of navigation guidance. The LIFT project at the Warsaw University of Technology serves as a practical example, showcasing a system designed to cater to people with special needs. This article presents a rule-based algorithm that generates navigation directions based on a trade-off between landmark references and spatial references in relation to the user’s location. The research uses a spatial data model consisting of three interconnected layers: the transport network, the room topology, and the building topography. The algorithm uses these data in subsequent stages. A defined set of rules generates redundant navigation directions for all potential decision points and then, subsequent rules filter and generalise them. To discuss the details of how the algorithm works, an example route is described in this study and the consequences of applying the selected rules are analysed. Next, a few problems that arose during the testing of the algorithm at Warsaw Metro stations are presented with proposed solutions. The results of the study made it possible to develop a mobile application, which is planned to be available to users by the end of 2024. Full article
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23 pages, 4087 KiB  
Article
SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization
by Khairul Mottakin, Kiran Davuluri, Mark Allison and Zheng Song
Sensors 2024, 24(19), 6327; https://doi.org/10.3390/s24196327 - 30 Sep 2024
Cited by 2 | Viewed by 2133
Abstract
Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual [...] Read more.
Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advanced Wireless Positioning and Sensing Technologies)
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12 pages, 2558 KiB  
Article
Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network
by Jaehyun Yoo
Sensors 2024, 24(17), 5698; https://doi.org/10.3390/s24175698 - 1 Sep 2024
Cited by 5 | Viewed by 1724
Abstract
Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, [...] Read more.
Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network. Full article
(This article belongs to the Special Issue Sensors and Techniques for Indoor Positioning and Localization)
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25 pages, 4182 KiB  
Article
W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots
by Dingji Luo, Yucan Huang, Xuchao Huang, Mingda Miao and Xueshan Gao
Sensors 2024, 24(17), 5662; https://doi.org/10.3390/s24175662 - 30 Aug 2024
Viewed by 1566
Abstract
In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of [...] Read more.
In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot’s body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results. Full article
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14 pages, 6445 KiB  
Article
Multi-Sensor-Assisted Low-Cost Indoor Non-Visual Semantic Map Construction and Localization for Modern Vehicles
by Guangxiao Shao, Fanyu Lin, Chao Li, Wei Shao, Wennan Chai, Xiaorui Xu, Mingyue Zhang, Zhen Sun and Qingdang Li
Sensors 2024, 24(13), 4263; https://doi.org/10.3390/s24134263 - 30 Jun 2024
Cited by 1 | Viewed by 1770
Abstract
With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This [...] Read more.
With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article
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21 pages, 3712 KiB  
Article
Towards Personally Relevant Navigation: The Differential Effects of Cognitive Style and Map Orientation on Spatial Knowledge Development
by Hannah Park, Manish K. Dixit and Fatemeh Pariafsai
Appl. Sci. 2024, 14(10), 4012; https://doi.org/10.3390/app14104012 - 9 May 2024
Cited by 1 | Viewed by 2236
Abstract
Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive [...] Read more.
Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive style might play an important role in creating a complete environmental representation and spatial navigation. However, this relationship between cognitive style and spatial navigation is not well researched. This study hypothesized that a specific type of map orientation (north-up vs. forward-up) might be more efficient for individuals with different cognitive styles. Forty participants were recruited to perform spatial tasks in a virtual maze environment to understand how cognitive style may relate to spatial navigation abilities, particularly the acquisition of survey and route knowledge. To measure survey knowledge, pointing direction tests and sketch map tests were employed, whereas, for route knowledge, the landmark sequencing test and route retracing test were employed. The results showed that both field-dependent and field-independent participants showed more accurate canonical organization in their sketch map task with a north-up map than with a forward-up map, with field-independent participants outperforming field-dependent participants in canonical organization scores. The map orientation did not influence the performance of Field-Independent participants on the pointing direct test, with field-dependent participants showing higher angular error with north-up maps. Regarding route knowledge, field-independent participants had more accurate responses in the landmark sequencing tests with a north-up map than with a forward-up map. On the other hand, field-dependent participants had higher accuracy in landmark sequencing tests in the forward-up map condition than in the north-up map condition. In the route retracing test, however, the map orientation had no statistically significant effect on different cognitive style groups. The results indicate that cognitive style may affect the relationship between map orientation and spatial knowledge acquisition. Full article
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35 pages, 13690 KiB  
Article
An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation
by Elfituri S. F. Lahemer and Ahmad Rad
Sensors 2024, 24(9), 2796; https://doi.org/10.3390/s24092796 - 27 Apr 2024
Cited by 2 | Viewed by 2735
Abstract
In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single [...] Read more.
In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot’s surroundings. Full article
(This article belongs to the Section Navigation and Positioning)
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15 pages, 3624 KiB  
Article
A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction
by Yu Hao, Fan Yang, Hao Huang, Shuaihang Yuan, Sundeep Rangan, John-Ross Rizzo, Yao Wang and Yi Fang
J. Imaging 2024, 10(5), 103; https://doi.org/10.3390/jimaging10050103 - 26 Apr 2024
Cited by 6 | Viewed by 4313
Abstract
People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive [...] Read more.
People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV. Full article
(This article belongs to the Special Issue Image and Video Processing for Blind and Visually Impaired)
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20 pages, 16490 KiB  
Article
An Up-View Visual-Based Indoor Positioning Method via Deep Learning
by Chen Chen, Yuwei Chen, Jianliang Zhu, Changhui Jiang, Jianxin Jia, Yuming Bo, Xuanzhi Liu, Haojie Dai, Eetu Puttonen and Juha Hyyppä
Remote Sens. 2024, 16(6), 1024; https://doi.org/10.3390/rs16061024 - 14 Mar 2024
Cited by 4 | Viewed by 2536
Abstract
Indoor positioning plays a crucial role in various domains. It is employed in various applications, such as navigation, asset tracking, and location-based services (LBS), in Global Navigation Satellite System (GNSS) denied or degraded areas. The visual-based positioning technique is a promising solution for [...] Read more.
Indoor positioning plays a crucial role in various domains. It is employed in various applications, such as navigation, asset tracking, and location-based services (LBS), in Global Navigation Satellite System (GNSS) denied or degraded areas. The visual-based positioning technique is a promising solution for high-accuracy indoor positioning. However, most visual positioning research uses the side-view perspective, which is susceptible to interferences and may cause concerns about privacy and public security. Therefore, this paper innovatively proposes an up-view visual-based indoor positioning algorithm. It uses the up-view images to realize indoor positioning. Firstly, we utilize a well-trained YOLO V7 model to realize landmark detection and gross extraction. Then, we use edge detection operators to realize the precision landmark extraction, obtaining the landmark pixel size. The target position is calculated based on the landmark detection and extraction results and the pre-labeled landmark sequence via the Similar Triangle Principle. Additionally, we also propose an inertial navigation system (INS)-based landmark matching method to match the landmark within an up-view image with a landmark in the pre-labeled landmark sequence. This is necessary for kinematic indoor positioning. Finally, we conduct static and kinematic experiments to verify the feasibility and performance of the up-view-based indoor positioning method. The results demonstrate that the up-view visual-based positioning is prospective and worthy of research. Full article
(This article belongs to the Section Engineering Remote Sensing)
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19 pages, 7874 KiB  
Article
An Autonomous Tracking and Landing Method for Unmanned Aerial Vehicles Based on Visual Navigation
by Bingkun Wang, Ruitao Ma, Hang Zhu, Yongbai Sha and Tianye Yang
Drones 2023, 7(12), 703; https://doi.org/10.3390/drones7120703 - 12 Dec 2023
Cited by 4 | Viewed by 5118
Abstract
In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous [...] Read more.
In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV’s environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the autonomous tracking and landing of UAVs. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAVs was established. Tracking performance was verified by tracking three trajectories of an unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance. Full article
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20 pages, 2526 KiB  
Article
Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning
by Ba-Viet Ngo, Thanh-Hai Nguyen and Chi Cuong Vu
Electronics 2023, 12(14), 3093; https://doi.org/10.3390/electronics12143093 - 17 Jul 2023
Cited by 2 | Viewed by 1561
Abstract
Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection [...] Read more.
Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair. Full article
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20 pages, 16867 KiB  
Article
Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals
by Xiaoyan Liu, Liang Chen, Zhenhang Jiao and Xiangchen Lu
Remote Sens. 2023, 15(13), 3229; https://doi.org/10.3390/rs15133229 - 22 Jun 2023
Cited by 2 | Viewed by 1631
Abstract
Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have [...] Read more.
Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning. Full article
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10 pages, 4236 KiB  
Communication
Towards Safe Visual Navigation of a Wheelchair Using Landmark Detection
by Christos Sevastopoulos, Mohammad Zaki Zadeh, Michail Theofanidis, Sneh Acharya, Nishi Patel and Fillia Makedon
Technologies 2023, 11(3), 64; https://doi.org/10.3390/technologies11030064 - 25 Apr 2023
Cited by 1 | Viewed by 2481
Abstract
This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be [...] Read more.
This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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30 pages, 12991 KiB  
Article
A Lightweight Approach to Localization for Blind and Visually Impaired Travelers
by Ryan Crabb, Seyed Ali Cheraghi and James M. Coughlan
Sensors 2023, 23(5), 2701; https://doi.org/10.3390/s23052701 - 1 Mar 2023
Cited by 7 | Viewed by 3121
Abstract
Independent wayfinding is a major challenge for blind and visually impaired (BVI) travelers. Although GPS-based localization approaches enable the use of navigation smartphone apps that provide accessible turn-by-turn directions in outdoor settings, such approaches are ineffective in indoor and other GPS-deprived settings. We [...] Read more.
Independent wayfinding is a major challenge for blind and visually impaired (BVI) travelers. Although GPS-based localization approaches enable the use of navigation smartphone apps that provide accessible turn-by-turn directions in outdoor settings, such approaches are ineffective in indoor and other GPS-deprived settings. We build on our previous work on a localization algorithm based on computer vision and inertial sensing; the algorithm is lightweight in that it requires only a 2D floor plan of the environment, annotated with the locations of visual landmarks and points of interest, instead of a detailed 3D model (used in many computer vision localization algorithms), and requires no new physical infrastructure (such as Bluetooth beacons). The algorithm can serve as the foundation for a wayfinding app that runs on a smartphone; crucially, the approach is fully accessible because it does not require the user to aim the camera at specific visual targets, which would be problematic for BVI users who may not be able to see these targets. In this work, we improve upon the existing algorithm so as to incorporate recognition of multiple classes of visual landmarks to facilitate effective localization, and demonstrate empirically how localization performance improves as the number of these classes increases, showing the time to correct localization can be decreased by 51–59%. The source code for our algorithm and associated data used for our analyses have been made available in a free repository. Full article
(This article belongs to the Section Sensing and Imaging)
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