TRS – Prof. Michael Kaess (Robotics Institute, Carnegie Mellon University) – Factor Graphs and Robust Perception

When: 3.11.2021 at 15:30

Where: Zoom

Abstract: Factor graphs have become a popular tool for modeling robot perception problems. Not only can they model the bipartite relationship between sensor measurements and variables of interest for inference, but they have also been instrumental in devising novel inference algorithms that exploit the spatial and temporal structure inherent in these problems. I will start with a brief history of these inference algorithms and relevant applications. I will then discuss open challenges in particular related to robustness from the inference perspective and discuss some recent steps towards more robust perception algorithms.

You can watch the seminar here