Development of an indoor positioning and navigation system using monocular SLAM and IMU

Yu Ching Mai, Ying Chih Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationFirst International Workshop on Pattern Recognition
EditorsXudong Jiang, Guojian Chen, Chiharu Ishii, Genci Capi
ISBN (Electronic)9781510604308
Publication statusPublished - 2016
Event1st International Workshop on Pattern Recognition - Tokyo, Japan
Duration: 2016 May 112016 May 13

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Other1st International Workshop on Pattern Recognition

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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