Minimum Energy Filtering for Collaborative Localisation
Australian National University, 2020
Mobile robotics is a rapidly growing field of research with applications across many industries. One of the foundational requirements for any mobile robot is having an accurate estimate of its position and orientation in space. This is a particularly challenging problem in GPS-denied environments and multi-vehicle collaborative localisation promises to be one potential solution. In this thesis proposal review, I will motivate a shift in thinking from traditional stochastic approaches to localisation (eg. Kalman Filter) to deterministic filtering. I will present some of my recent work on using deterministic minimum-energy filtering for collaborative localisation and discuss my research plans for the future.
About the Thesis Proposal Review
The thesis propsal review (TPR) is a major milestone at the end of a PhD candidate’s first year. It consists of a written report detailing the candidate’s proposed research topic and a plan for future work. It also includes a comprehensive literature review of the field and a recount of progress achieved to date. In addition to the report, the PhD candidate also presents their TPR in a public seminar, followed by audience questions and then a private discussion with the supervisory panel.
Due to the Coronavirus restrictions and the closure of the university, my seminar was held as an online teleconference. The slides for the presentation can be downloaded here, and a recording of the presentation is below.