Abstract
A self-driving electric wheelchair must estimate its own position, identify the travelable area, and determine the travel route. For indoor self-position estimation, the location can be estimated by wireless LAN or beacon. However, for autonomous operation, more accurate position estimation is required. Conventionally, we have used augmented reality (AR) markers with high positioning accuracy as a method for self-position estimation of a wheelchair in a relatively narrow space. However, the positioning error of the angle when the AR marker was recognized from a frontal direction was large, and its improvement was a problem. In this research, we propose a method to correct the errors of positions obtained with AR markers using wheelchair odometry information and object detection. Since estimates using AR markers and odometry information both have errors, we propose a method for correcting the AR marker positioning by odometry information and distance information obtained by object detection. Preliminary experiments showed that using odometry for correction improves the positioning error and allows for stable control of the wheelchair.
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Part of this work was carried out under the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University.
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Sato, F. (2020). Improvement of Self Position Estimation of Electric Wheelchair Combining Multiple Positioning Methods. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_51
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DOI: https://doi.org/10.1007/978-3-030-15032-7_51
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