Past talk · AI/ML Seminar Series
End-to-end Learnable Particle Filters and Smoothers
PhD Student, Department of Computer Science, University of California, Irvine
- Date & time
- Monday, December 2, 2024 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
Estimating the temporal state of a system from image sequences is an important task for many vision and robotics applications. A number of classical frameworks for state estimation have been proposed, but often these methods require human experts to specify the system dynamics and measurement model, requiring simplifying assumptions that hurt performance. In this presentation, I will develop end-to-end learnable particle filters and particle smoothers, and show how to bring classic state estimation methods into the age of deep learning. We first create an end-to-end learnable particle filter that uses flexible neural networks to propagate multimodal, particle-based representations of state uncertainty. We then expand on our particle filtering method to create the first end-to-end learnable particle smoother, which incorporates information from future as well as past observations, and apply this particle smoother to the real-world task of city-scale geo-localization using camera and planimetric map data.
About the speaker
Ali Younis is a final-year PhD student in Computer Science at UCI, advised by Prof. Erik Sudderth. He previously completed his bachelor's and master's degrees at UCI and briefly worked on spacecraft systems before returning for a PhD. He is broadly interested in particle based belief propagation systems for time varying systems with applications in computer vision.