Fast fingerprint database maintenance for indoor positioning based on UGV SLAM.

Area of Science:

• Robotics and Navigation
• Wireless Sensor Networks
• Geomatics Engineering

Background:

• Indoor positioning systems (IPS) are crucial, with Received Signal Strength Indicator (RSSI) fingerprinting of Signals of Opportunity (SOP) being a viable method.
• Maintaining RSSI fingerprint databases is challenging due to signal variability and environmental changes.
• Existing methods for database updates are often time-consuming and inefficient.

Purpose of the Study:

• To develop a rapid and effective solution for updating indoor fingerprint databases.
• To enhance the accuracy and efficiency of indoor positioning systems.
• To enable real-time map generation and environmental change detection for indoor navigation.

Main Methods:

• Utilized a self-developed Unmanned Ground Vehicle (UGV) platform named NAVIS.
• Integrated multiple SOP sensors on NAVIS: digital compass, light sensor, and smartphone for WiFi RSSI collection.
• Implemented real-time interpolation and updating of the SOP fingerprint database during indoor mapping by the UGV.

Main Results:

• The NAVIS platform enabled rapid creation and updating of fingerprint databases at a high sampling frequency (5Hz) with denser reference points.
• Indoor maps were generated autonomously without requiring prior environmental information.
• The system demonstrated quick detection of environmental changes, crucial for robust fingerprint-based indoor positioning.

Conclusions:

• The proposed UGV-based method significantly improves the speed and efficiency of indoor fingerprint database maintenance.
• Real-time updating and autonomous map generation enhance the practicality of SOP fingerprinting for indoor navigation.
• This approach offers a promising solution for dynamic indoor environments requiring continuous positioning accuracy.