In this article, we present our work regarding the development of advanced driver-assistance systems for an electric-powered wheelchair. Our project aims at improving the autonomy of people with reduced mobility. After conducting a clinical study, we identified several use-cases. In this paper, we introduce the detection, localization and tracking of points of interest in the immediate surroundings of the chair in an indoor environment, i. The aim is not only to improve perception around the chair but also to enable semi-autonomous driving towards these targets.
First, we introduce a repurposing of YOLOv3, the object detection algorithm, to our use case. Then, we show our use of the Intel Realsense camera for depth estimation. To validate our approach, we realized several experiments in a controlled indoor environment.
The detection, distance estimation, and tracking pipeline is tested using our custom dataset. This includes corridors, doors, handles, and switches. One of the scenarios studied to validate the proposed platform includes not only the detection and tracking of objects but also the movement of the wheelchair towards one of these points of interest.
Detection, classification, positioning, and tracking of objects are necessary tasks in the field of mobile robotics. Nowadays, these tasks often rely on computer vision and are carried using various sensor modalities camera, LIDAR, RADAR and processing algorithms pattern recognition, filters, classification, feature extraction, etc.
In this paper, we focused our work on the object detection, localization and tracking of interest points on an electric-wheelchair-type robotic platform. Our contribution aims to develop object detection, depth estimation, and target tracking for the wheelchair, using deep learning approaches.
