Radio Frequency Simultaneous Localisation and Mapping (RF SLAM) for Robot Navigation

To develop autonomous navigation system based on ubiquitous RF signals
Description of the Project: 

Robots and UAVs are often required to operate (semi)autonomously, especially when there is limited human awareness for safe operation. In such cases, self-navigation functionalities are required. This proposal relates to the adaptation of RF signals of opportunity for navigation using SLAM approaches.

The relatively low strength of GNSS signals means that they are susceptible to various forms of interference ranging from multipath effects to intentional jamming. RF signals used for various applications (e.g., Wi-Fi, Digital Radio, Digital TV) are ubiquitous and are attractive for navigation in GNSS-denied scenarios (e.g., in dense urban environments with high-rise buildings, and indoors). While there are existing works on isolated or combined usage of RF signal sources for navigation, this project proposes a holistic simultaneous localisation and mapping approach involving coordinated “crowdsourcing” with multiple drones carrying RF receivers.

RF Data Collection - Offline Stage

The proposed RF SLAM will involve sampling and recording every available information (mostly, cell IDs and received signal strength) from multiple RF signal sources against position data obtained from GNSS receivers. This will be the offline stage where a database of RF signatures over wide designated areas will be created. The data collected by each drone will be uploaded to the cloud if internet connection is available or shared with a nearby drone.

RF-Based Positioning - Online Stage

Once the database is created in the offline stage, a navigating drone (a drone in service, e.g., delivering parcels) will be able to take fresh measurements of available RF signals and compare these with the database to resolve its location while at the same time updating the database with any new data collected – resulting in improved database and by extension, more accurate positional calculation over time.

Augmentation with Other Sensors

While RF signal sources will be the target, however, where there are other sensors (e.g., magnetometers and image sensors), these will be used to improve accuracy in mapping and locating where possible. Moreover, GIS data will be used if available.

Resources required: 
• Development boards • RF connectivity modules • Software-Defined Radio modules and antennas • Drones and/or robots (or access to existing ones)
Project number: 
First Supervisor: 
University of Edinburgh
Second Supervisor(s): 
First supervisor university: 
University of Edinburgh
Essential skills and knowledge: 
• Proficiency in RF concepts • Experience with the Linux environment • Experience with digital signal processing techniques • Proficiency in Python and C/C++ programming language
Desirable skills and knowledge: 
• Experience with robot/robotic platform development • Familiarity with GNU radio design tool
Funding Available: