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Michael Kaess: Factor Graphs for Robot Perception | CMU RI Seminar


curated by | September 22, 2018

Link to video on YouTube

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 overview some of the inference algorithms and present two specific applications: Simultaneous localization and mapping for underwater robots and state estimation for aerial robots. For state estimation I will introduce a novel fixed-lag smoother for visual-inertial odometry. I will also give a brief overview of factor graphs in the context of other robot perception problems.”

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