(Georgia Institute of Technology, 2008)
Ranganathan, Ananth; Dellaert, Frank
Topological maps are graphical representations of
the environment consisting of nodes that denote landmarks, and
edges that represent the connectivity between the landmarks.
Automatic detection of landmarks, usually special places in the
environment such as gateways, in a general, sensor-independent
manner has proven to be a difficult task. We present a landmark
detection scheme based on the notion of “surprise” that addresses
these issues. The surprise associated with a measurement is
defined as the change in the current model upon updating it
using the measurement. We demonstrate that surprise is large
when sudden changes in the environment occur, and hence, is a
good indicator of landmarks. We evaluate our landmark detector
using appearance and laser measurements both qualitatively
and quantitatively. Part of this evaluation is performed in the
context of a topological mapping algorithm, thus demonstrating
the practical applicability of the detector.
(Georgia Institute of Technology, 2005)
Oh, Sang Min; Rehg, James M.; Dellaert, Frank
We introduce Segmental Switching Linear Dynamic Systems (S-SLDS), which improve on standard
SLDSs by explicitly incorporating duration modeling capabilities. We show that S-SLDSs can adopt
arbitrary finite-sized duration models that describe data more accurately than the geometric distributions
induced by standard SLDSs. We also show that we can convert an S-SLDS to an equivalent standard
SLDS with sparse structure in the resulting transition matrix. This insight makes it possible to adopt
existing inference and learning algorithms for the standard SLDS models to the S-SLDS framework. As
a consequence, the more powerful S-SLDS model can be adopted with only modest additional effort in
most cases where an SLDS model can be applied. The experimental results on honeybee dance decoding
tasks demonstrate the robust inference capabilities of the proposed S-SLDS model.