Viorela Ila: Fundamental Methodologies for Large-scale Robotic Applications
L220, 9:00 3.2.2012
This seminar will summarize some theoretical contributions in the field
of simultaneous localization and mapping (SLAM), which enable long-term
robotic navigation. Those are methods that I've been developing during
the last three years.
First methodology is a filtering approach to pose SLAM, a variant of
SLAM that estimates robot position rather than landmarks in the
environment. It uses information theory to select only informative links
and relevant robot poses and the result is a compact representation of
the SLAM problem.
Looking at SLAM in terms of graphs has led to several novel and exciting
developments in the past few years. Subgraph preconditioned conjugate
gradient (SPCG) is obtained by re-interpreting the method of conjugate
gradients in terms of the graphical model representation of the SLAM
problem. It combines the advantages of direct and iterative methods for
solving linear systems. This led to important computational speed-up in
large scale SLAM and structure from motion (SFM) applications.
The Bayes tree is another concept we developed at Georgia tech. It
provides an algorithmic foundation which enable a better understanding
of existing graphical model inference algorithms and their connection to
sparse matrix factorization methods. Based on that, we derived a fully
incremental smoothing and mapping algorithm for efficient online robot
localization and mapping of the environment called iSAM2.
SpeakersIla Viorela S., Ing., Ph.D.