Research Overview

This research project addresses the critical need for enhancing mobility for wheelchair users by focusing on real-time segmentation of the ground plane while excluding obstacles. The significance of this project lies in overcoming the challenges faced by wheelchair users in navigating diverse environments, particularly in scenarios where smooth movement is essential for their independence and quality of life.

The objective of this research is to develop a segmentation model within the SAM framework capable of accurately delineating the ground surface to facilitate uninterrupted movement for wheelchair users. This study utilizes adaptive learning algorithms and machine learning techniques to discern and isolate the ground plane from its surroundings. Real-time data collected from a camera sensor mounted on a mobile platform is processed through the SAM model to achieve segmentation.

Initial experiments demonstrate promising results, showcasing the SAM model's capability to accurately segment the ground plane while maintaining computational efficiency. Rigorous testing in various environmental conditions further validates the effectiveness of the segmentation model in facilitating smooth movement for wheelchair users.

The implications of this research extend to enhancing accessibility and mobility for wheelchair users in diverse settings, including indoor and outdoor environments. By harnessing advanced technology, this project aims to improve the quality of life and increase autonomy for individuals with mobility impairments. Future work will focus on further refining the segmentation model and integrating it into practical applications to support wheelchair users in their daily lives.