Monitoring-Camera-Assisted SLAM for Indoor Positioning and Navigation

Abstract

In the information age, intelligent indoor positioning and navigation services are required in many application scenarios. However, most current visual positioning systems cannot function alone and have to rely on additional information from other modules. Nowadays, public places are usually equipped with monitoring cameras, which can be exploited as anchors for positioning, thus enabling the vision module to work independently.

In this thesis, a high-precision indoor positioning and navigation system is proposed, which integrates monitoring cameras and smartphone cameras. Firstly, based on feature matching and geometric relationships, the system obtains the transformation scale from relative lengths in the cameras’ perspective to actual distances in the floor plan. Secondly, by scale transformation, projection, rotation and translation, the user's initial position in the real environment can be determined. Then, as the user moves forward, the system continues to track and provide correct navigation prompts.

The designed system is implemented and tested in different application scenarios. It is proved that our system achieves a positioning accuracy of 0.46m and a successful navigation rate of 90.6%, which outperforms the state-of-the-art schemes by 13% and 3% respectively. Moreover, the system latency is only 0.2s, which meets the real-time demands.

In summary, assisted by widely deployed monitoring cameras, our system can provide users with accurate and reliable indoor positioning and navigation services.