Lightweight and Effective Visual Localization in Changing Environments for Resource-Constrained Mobile Robots

Supervisory Team:   Dr Shoaib Ehsan

Project description

Visual place recognition (VPR) is the process of recognizing a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localization, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities.

The main aim of this project is to research vision-based localization algorithms and systems that would allow resource-constrained robots and mobile devices to understand their position in an environment over long periods. With such understanding, robots can become more autonomous in building and maintaining their map of the environment so that they can collaborate and become more useful for humans. As an example, we will focus on investigating approaches that are suitable for small unmanned aerial vehicles with restricted payload onboard and as a result, limited computational capabilities. Deep learning approaches addressing VPR perform well under isolated variations in appearance. The power of these methods, however, stems from specific training on the expected scene variations and complex computational effort. This, in turn, imposes the need for extensive training datasets and powerful Graphics Processing Units (GPUs), which are often unavailable onboard small aircraft, rendering the use of such methods impractical in aerial navigation. Inspired by the need for lightweight and effective techniques for VPR onboard small aircraft, this project will investigate novel methods capable of coping with the variability of places when experienced from such small aircraft, while bounding the onboard computation effort for real-time operation.

If you wish to discuss any details of the project informally, please contact Dr Shoaib Ehsan, AIC Research Group, Email: [Email Address Removed]

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 December 2022 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free per annum for up to 3.5 years.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering, next page select “PhD Computer Science (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Shoaib Ehsan

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: [Email Address Removed]