Kaveh Fathian - CCTA 2021 Workshop on The Confluence of Vision and Control
Channel: Kaveh Fathian
Date: 2021-08-22

Title: Graph-Theoretic Frameworks for Robust Data Association

Description: Data association is concerned with matching elements of two (or multiple) sets of data that are measured by sensors or known based on prior knowledge. As such, it includes a broad range of control and robotic applications, including estimating camera motion, loop-closure in SLAM, and map merging based on feature correspondences in images. The main challenge in data association is the existence of wrong matches, which occur due to noise, outliers, or similar-looking features, and if not corrected can drastically affect the results in these applications. In this talk, we review classical and recent techniques for robust data association, ranging from model-based methods between two sets (e.g, RANSAC) to model-free techniques based on the notion of cycle consistency across multiple sets of data. We show that these techniques can achieve considerable improvement in the accuracy of existing pipelines such as SLAM.

Biosketch: Kaveh Fathian received the M.S. degree and the Ph.D. degree from the University of Texas at Dallas, TX, USA, in 2013 and 2018, respectively, both in electrical engineering. He also received the M.S. degree in mathematics from the University of Texas at Dallas, in 2018. He is a research scientist at the Massachusetts Institute of Technology’s Aerospace Controls Laboratory, Cambridge, MA, USA. His current research interests include topics in control theory, distributed and multi-robot systems, vision-based estimation, and spectral graph theory. Dr. Fathian is a Member of the IEEE Control Systems Society and the Robotics and Automation Society.