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Proceedings Paper

Development of an indoor positioning and navigation system using monocular SLAM and IMU
Author(s): Yu-Ching Mai; Ying-Chih Lai
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Paper Abstract

The positioning and navigation systems based on Global Positioning System (GPS) have been developed over past decades and have been widely used for outdoor environment. However, high-rise buildings or indoor environments can block the satellite signal. Therefore, many indoor positioning methods have been developed to respond to this issue. In addition to the distance measurements using sonar and laser sensors, this study aims to develop a method by integrating a monocular simultaneous localization and mapping (MonoSLAM) algorithm with an inertial measurement unit (IMU) to build an indoor positioning system. The MonoSLAM algorithm measures the distance (depth) between the image features and the camera. With the help of Extend Kalman Filter (EKF), MonoSLAM can provide real-time position, velocity and camera attitude in world frame. Since the feature points will not always appear and can't be trusted at any time, a wrong estimation of the features will cause the estimated position diverge. To overcome this problem, a multisensor fusion algorithm was applied in this study by using the multi-rate Kalman Filter. Finally, from the experiment results, the proposed system was verified to be able to improve the reliability and accuracy of the MonoSLAM by integrating the IMU measurements.

Paper Details

Date Published: 11 July 2016
PDF: 8 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111L (11 July 2016); doi: 10.1117/12.2242920
Show Author Affiliations
Yu-Ching Mai, National Cheng Kung Univ. (Taiwan)
Ying-Chih Lai, Feng Chia Univ. (Taiwan)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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