Abstract
High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.
Highlights
With the development of society, high-precision positioning has become indispensable in many places
An algorithm based on 3D map matching and visual inertial odometry (VIO) system is proposed for indoor localization
Field model, the output by the VIO system are input into the conditional random field (CRF) model as observation information, and the optimal state points sequence is solved
Summary
With the development of society, high-precision positioning has become indispensable in many places. Visual inertial odometry is a well-known technology that uses cameras and inertial measurement unit (IMU) to estimate the position and motion trajectory of pedestrians or robots It combines the information of vision and IMU to obtain higher accuracy location information than single vision and single IMU [13]. Proposes an offline map matching algorithm designed for indoor localization systems based on CRF. We propose an indoor positioning algorithm that combines 3D map matching based on a CRF and the VIO system, and uses the optimal matching states sequence to correct the VIO trajectory. An algorithm based on 3D map matching and VIO system is proposed for indoor localization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
