TY - GEN
T1 - Development of an indoor positioning and navigation system using monocular SLAM and IMU
AU - Mai, Yu Ching
AU - Lai, Ying Chih
N1 - Funding Information:
This study was supported in part by the Ministry of Science and Technology of Taiwan under grant number MOST 103-2632- E-035-001-MY3.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2242920
DO - 10.1117/12.2242920
M3 - Conference contribution
AN - SCOPUS:84983089391
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - First International Workshop on Pattern Recognition
A2 - Jiang, Xudong
A2 - Chen, Guojian
A2 - Ishii, Chiharu
A2 - Capi, Genci
PB - SPIE
T2 - 1st International Workshop on Pattern Recognition
Y2 - 11 May 2016 through 13 May 2016
ER -