# Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People

arXiv: https://arxiv.org/abs/1904.13037 · HTML: https://mlsanity.com/paper/1904.13037/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor](https://mlsanity.com/paper/2204.01154/) (2022)
- [Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster](https://mlsanity.com/paper/2112.13194/) (2021)
- [Advanced Audio Aid for Blind People](https://mlsanity.com/paper/2212.00004/) (2022)
- [Classroom Slide Narration System](https://mlsanity.com/paper/2201.08574/) (2022)
- [SLAM for Visually Impaired People: a Survey](https://mlsanity.com/paper/2212.04745/) (2022)
- [DISHA: Low-Energy Sparse Transformer at Edge for Outdoor Navigation for the Visually Impaired Individuals](https://mlsanity.com/paper/2406.15864/) (2024)
- [A Waste Copper Granules Rating System Based on Machine Vision](https://mlsanity.com/paper/2207.04575/) (2022)

## Abstract (arXiv)

This paper presents a wearable assistive device with the shape of a pair of eyeglasses that allows visually impaired people to navigate safely and quickly in unfamiliar environment, as well as perceive the complicated environment to automatically make decisions on the direction to move. The device uses a consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU) to detect obstacles. As the device leverages the ground height continuity among adjacent image frames, it is able to segment the ground from obstacles accurately and rapidly. Based on the detected ground, the optimal walkable direction is computed and the user is then informed via converted beep sound. Moreover, by utilizing deep learning techniques, the device can semantically categorize the detected obstacles to improve the users' perception of surroundings. It combines a Convolutional Neural Network (CNN) deployed on a smartphone with a depth-image-based object detection to decide what the object type is and where the object is located, and then notifies the user of such information via speech. We evaluated the device's performance with different experiments in which 20 visually impaired people were asked to wear the device and move in an office, and found that they were able to avoid obstacle collisions and find the way in complicated scenarios.