[robotics-worldwide] ACC Tutorial: Stochastic Models, Information Theory, and Lie Groups
Gregory S Chirikjian
gchirik1 at jhu.edu
Tue Apr 9 06:42:01 PDT 2013
ACC Tutorial Sunday, June 16, 2013, Washington DC
Tutorial: Stochastic Models, Information Theory, and Lie Groups
Organizer: Gregory Chirikjian, Johns Hopkins University
This tutorial reviews stochastic models in robot motion planning, state estimation, and control, and explains how concepts from both information theory and the theory of Lie groups are naturally intertwined with these models. Unlike most treatments of continuous time stochastic processes, which are either highly formalized or focusing on one-dimensional problems, the review provided here will cover physically motivated multi-dimensional problems in an accessible way.
Topics such as the relationship between nonholonomic constraints, white noise forcing, and the parametric distributions that result, will be discussed. The inter-conversion of Ito and Stratonovich stochastic differential equations will be explained. Some elementary Lie group theory will be used.
Application areas will include orientation and pose estimation, simultaneous localization and mapping, and steering flexible needles for minimally invasive surgical applications. As time permits other topics will be discusses including stochastic modeling of biological macromolecules from a systems-theoretic perspective.
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