Autonomous Movement of Wheelchair by Cameras and YOLOv7

Abstract

A wheelchair can provide limited but crucial mobility to an injured or disabled individual. This paper presents the first stage of the development of a smart wheelchair which is the customization of a manually controlled wheelchair with a novel implementation of octascopic vision. This relatively inexpensive design of an autonomous wheelchair consists of two monochromic camera arrays (each having four cameras) placed around the frame of the wheelchair to achieve a view of 360 degrees. The initial research goal was to design a wheelchair controlled by the embedded processor, allowing the wheelchair to navigate autonomously around an indoor facility with and without human intervention. Additionally, it was intended to allow those previously denied access to the world of automatic wheelchairs because of a low personal income. Through the testing of wheelchair functionality, (a) a large dataset of octascopic images was captured from this wheelchair, and (b) a YOLOv7-based object detection model was developed to avoid obstacles and autonomously control the movement. This paper presents the camera placement and the obstacle detection model using octascopic images. All the project design files have been granted an open-source license and can be reproduced publicly.

Keywords

• autonomous movement
• deep Learning
• machine vision
• obstacle detection
• wheelchair
• YOLOv7