This Friday’s Forum for AI features Pat Beeson and Joseph Modayil, fellow grad students from my lab, talking about robot map building using hybrid metrical/topological maps. These are maps that describe large-scale space as a coarse-grained topological graph of places connected by path segments. The small-scale space around each place is represented by a fine-grained metrical map that describes the local area in detail. Here’s description and an excerpt from the abstract:
Hybrid Mapping Models:
Bridging the Gap between Robot Sensors and Symbolic Spatial Representations
Patrick Beeson and Joseph Modayil
Friday, April 23, 2004
… Our lab, along with other researchers, have looked into how to build, check, and order topological map models given reliable actions and perceptions in the world. The problem with topological maps has always been in the way they ground the robot’s sensory experience. This is often done in an ad-hoc way for each individual robot sensory setup. There is often little overlap in the approaches used throughout the community.
Today, our lab is joining a handful of other researchers in developing hybrid mapping techniques. We are promoting the use of metrical models to describe “small-scale space”: akin to a human’s ability to have a detailed model of their local surround. Topological maps are useful for representing “large-scale space”: graph structures imply compact, hierarchical, symbolic descriptions useful for planning, communication, and storing multiple hypotheses.
Our talk will focus on how these two ontologies interact. In particular, we discuss what new concepts are necessary to move from detailed metrical descriptions of bounded local regions to graph-like structures of large, complex environments. We will present published work that shows several of the advances we have made in recent months as well as present the open questions that still need to be examined in more detail.