True scaled 6 DoF egocentric localisation with monocular wearable systems

Gutiérrez-Gómez, Daniel; Guerrero, J. J.

doi:10.1016/j.imavis.2016.05.015

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…However, as the camera moves and old landmarks are lost while new ones are initialised, the scale of the scene changes continuously. To address this issue in large environments, Gutierrez-Gomez and Guerrero (2016) proposed an approach that computes the true scale dynamically using visual odometry estimates from wearable single cameras. Their method relies on the characteristic oscillatory movement of the human body during walking to extract scale information, making it particularly suitable for wearable systems.…”

Section: Simultaneous Localisation and Mapping (Slam)mentioning

confidence: 99%

An Outlook into the Future of Egocentric Vision

Plizzari,

Goletto,

Furnari

et al. 2024

Int J Comput Vis

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What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.

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Section: Simultaneous Localisation and Mapping (Slam)mentioning

confidence: 99%

An Outlook into the Future of Egocentric Vision

Plizzari,

Goletto,

Furnari

et al. 2024

Int J Comput Vis

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“…Gutierrez-Gomez et al [44] estimate walking speed from the frequency of body oscillation using a single camera that can be worn on the head (helmet in their experiment) or chest level. Through this process, they estimate a map and visual audiometry.…”

Section: Computer Vision-based Approachesmentioning

confidence: 99%

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

et al. 2022

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Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfamiliar area without external assistance. The design space for assistive technologies for the visually impaired is complex, involving many design parameters including reliability, transparent object detection, handsfree operations, high-speed real-time operations, low battery usage, low computation and memory requirements, ensuring that it is lightweight, and price affordability. State-of-the-art visually impaired devices lack maturity, and they do not fully meet user satisfaction, thus more effort is required to bring innovation to this field. In this work, we develop a pair of smart glasses called LidSonic that uses machine learning, LiDAR, and ultrasonic sensors to identify obstacles. The LidSonic system comprises an Arduino Uno device located in the smart glasses and a smartphone app that communicates data using Bluetooth. Arduino collects data, manages the sensors on smart glasses, detects objects using simple data processing, and provides buzzer warnings to visually impaired users. The smartphone app receives data from Arduino, detects and identifies objects in the spatial environment, and provides verbal feedback about the object to the user. Compared to image processing-based glasses, LidSonic requires much less processing time and energy to classify objects using simple LiDAR data containing 45-integer readings. We provide a detailed description of the system hardware and software design, and its evaluation using nine machine learning algorithms. The data for the training and validation of machine learning models are collected from real spatial environments. We developed the complete LidSonic system using off-the-shelf inexpensive sensors and a microcontroller board costing less than USD 80. The intention is to provide a design of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. This work is expected to open new directions for smart glasses design using open software tools and off-the-shelf hardware.

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“…Wall49,52,46,46,46,50,51,50,52,49,53,52,50,50,55,54,57,55,55,57,58,56,57,58,62,62,62,63,65,64,66,66,69,70,78,80,83,83,84,86,88,93,95,96,102, 111,123,126,118,120,120,120,122,…”

mentioning

confidence: 99%

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed¹,

Katib²,

Albeshri³

et al. 2022

Preprint

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Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

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True scaled 6 DoF egocentric localisation with monocular wearable systems

Cited by 6 publications

References 47 publications

An Outlook into the Future of Egocentric Vision

An Outlook into the Future of Egocentric Vision

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

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