(Georgia Institute of Technology, 2011-01-24)
Ranganathan, Ananth; Dellaert, Frank
We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an
environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping
(OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given
measurements. With each successive measurement, the posterior is updated incrementally using a Rao–Blackwellized
particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies
that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is
demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping
algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art
significantly. We evaluate the algorithm on a robot in diverse environments.