Abstract
Abstract Powered wheelchair users often struggle to drive safely and accompanied by a caregiver. To improve assistance efficiency and reduce caregiver’s load, the wheelchair needs to autonomously follow the caregiver in the surroundings. In this paper, we present an approach to simultaneous localization and human following for a wheelchair robot equipped with Microsoft Kinect that captures RGB images along with per-pixel depth information (RGB-D camera). The speeded-up robust feature (SURF) algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on the environmental SURF features, we utilize an extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) to estimate robot pose. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Then target person can be recognized and tracked by combining SURF feature matching with a specified identity signature and human detection results. Moreover, a fuzzy based controller provides dynamical human following for the wheelchair robot with a desired interval. Consequently, the experimental results demonstrated the effectiveness and feasibility in real world environments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.