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Autonomous Unmanned Aerial Systems (UAS) navigation in GPS denied scenarios

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Autonomous Unmanned Aerial Systems (UAS) navigation is more complex in GPS denied scenarios. In this project, Simultaneous Localization and Mapping (SLAM) approach for the UAS localization to aid autonomous navigation will be implemented. A variety of sensors including IMU, frame based cameras and dynamic vision sensors will be used. A novel fusing technique based on deep learning will be investigated to compute a full 6 DOF pose estimate. This will then be integrated with a SLAM approach to aid GPS denied autonomous navigation.

In [1] a measurement model based on the Hough transform is proposed for extracting range and bearing information from simple depth imaging system, and a 3D Mapping technique for processing of high-noise images named Boundary Searching Hough transform is developed by the author and proved to be the most robust technique for this purpose. As part of our proposed approach, a novel filtering methodology is developed to separate static and dynamic accelerations. The filter allows computation of a full 6 DOF pose estimate from a 3-axis accelerometer, suitable for application in GPS-Denied scenarios. This development will be utilized in this proposed project.

The project objectives are,
• Define specific requirements for GPS denied autonomous navigation.
• Investigate deep learning approach for data fusion.
• Develop a technique to integrate the fused data and SLAM approach.
• Testing and validation of the developed GPS denied autonomous navigation.

Funding Notes

There is no funding for this project: applications can only be accepted from self-funded candidates


[1] A. Harman, O. Duran, and Y. Zweiri, Novel Model for SLAM in Unmanned Aerial Vehicles, International Conference on Intelligent Robotics and Applications (ICIRA 2015), Portsmouth, UK, August 24th -27th, 2015.

How good is research at Kingston University in General Engineering?

FTE Category A staff submitted: 14.00

Research output data provided by the Research Excellence Framework (REF)

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