(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, 2004)
Ranganathan, Ananth; Dellaert, Frank
Robotic mapping involves finding a solution to the correspondence problem. A general purpose solution to this problem is as yet unavailable due to the combinatorial nature of the state space. We present a framework for computing the posterior distribution over the space of topological maps that solves the correspondence problem in the context of topological mapping. Since exact inference in this space is intractable, we present two sampling algorithms that compute sample-based representations of the posterior. Both the algorithms are built on a Bayesian product partition model that is derived from the mixture of Dirichlet processes model. Robot experiments demonstrate the applicability of the algorithms.