A Navigation and Augmented Reality System for Visually Impaired People

Abstract

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People

“…When designing their systems, researchers had to find the right balance between those points, depending on the purpose at which they were aiming. One of the most common solutions is to use smartphones equipped with RGB-D or monocular cameras and position sensors (accelerometers, gyroscopes, and magnetometers), as shown in examples [27][28][29]33,42,44]. Another solution is the introduction of infra-red or laser sensors in the acquisition system, a choice made in articles [23,32,45].…”

“…The most popular choice is audio-feedback through speech instructions or sonification guidance (the use of sound or music to depict different elements present in a given scene). A tactile interface, such as a Braille display [22], or haptic systems, such as a vibrating smartphone [16,42], are among other possibilities. The most effective solution seems to be a combination of several feedback interfaces to adapt to the different situations encountered by users.…”

“…Another solution is bone-conducting earphones that allow the delivery of instructions without covering the ears, therefore not impairing sound perception and offering the possibility to communicate with other people. The devices for the other types of feedback are as follows: motors on a cane [33], vibrating belt and Braille interface [22], two smartwatches [46], and smartphone vibrations [16,42].…”

“…The Kalman filter algorithm (Linear Quadratic Estimation) is a method to estimate unknown variables from the observation of a series of measurements that can be employed for many tasks, such as pose estimation [26], obstacle motion estimation [33], or error reduction [42].…”

An Overview of Machine Learning and 5G for People with Disabilities

“…A smartphone-based solution [11] helps the visually impaired navigate along predefined paths in indoor and outdoor scenarios. It does so by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.…”