(Georgia Institute of Technology, 2010)
Alcantarilla, Pablo F.; Oh, Sang Min; Mariottini, Gian Luca; Bergasa, Luis M.; Dellaert, Frank
We aim to perform robust and fast
vision-based localization using a pre-existing large
map of the scene. A key step in localization is associating the features extracted from the image with
the map elements at the current location. Although
the problem of data association has greatly benefited
from recent advances in appearance-based matching
methods, less attention has been paid to the effective
use of the geometric relations between the 3D map
and the camera in the matching process.
In this paper we propose to exploit the geometric
relationship between the 3D map and the camera
pose to determine the visibility of the features. In
our approach, we model the visibility of every map
feature w.r.t. the camera pose using a non-parametric
distribution model. We learn these non-parametric
distributions during the 3D reconstruction process,
and develop efficient algorithms to predict the visibility of features during localization. With this approach,
the matching process only uses those map features
with the highest visibility score, yielding a much
faster algorithm and superior localization results. We
demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the
localization in large and cluttered environments.
(Georgia Institute of Technology, 2005-07)
Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank
Switching Linear Dynamic System (SLDS) models are
a popular technique for modeling complex nonlinear dynamic
systems. An SLDS has significantly more descriptive
power than an HMM, but inference in SLDS models is
computationally intractable. This paper describes a novel
inference algorithm for SLDS models based on the Data-
Driven MCMC paradigm. We describe a new proposal
distribution which substantially increases the convergence
speed. Comparisons to standard deterministic approximation
methods demonstrate the improved accuracy of our new
approach. We apply our approach to the problem of learning
an SLDS model of the bee dance. Honeybees communicate
the location and distance to food sources through a
dance that takes place within the hive. We learn SLDS
model parameters from tracking data which is automatically
extracted from video. We then demonstrate the ability to
successfully segment novel bee dances into their constituent
parts, effectively decoding the dance of the bees.
(Georgia Institute of Technology, 2005-06)
Dellaert, Frank; Kwatra, Vivek; Oh, Sang Min
We introduce mixture trees, a tree-based data-structure
for modeling joint probability densities using a greedy hierarchical
density estimation scheme. We show that the mixture
tree models data efficiently at multiple resolutions, and
present fast conditional sampling as one of many possible
applications. In particular, the development of this datastructure
was spurred by a multi-target tracking application,
where memory-based motion modeling calls for fast
conditional sampling from large empirical densities. However,
it is also suited to applications such as texture synthesis,
where conditional densities play a central role. Results
will be presented for both these applications.