Exploration and Reasoning in Large-Scale and Visual Spaces
Benjamin Kuipers
Computer Sciences Department
The University of Texas at Austin
Abstract
We have developed the Spatial Semantic Hierarchy (SSH) as a
heterogeneous representation for knowledge of large-scale space: the
cognitive map. Each level of the SSH has its own descriptive ontology
and its own mathematical foundation. The objects, relations, and
assumptions at each level are abstracted from the levels below. The
control level allows the robot and its environment to be formalized as
a continuous dynamical system, whose stable equilibrium points can be
abstracted to a discrete set of ``distinctive states.'' Trajectories
linking these states can be abstracted to actions, giving a discrete
causal graph representation of the state space. The causal graph of
states and actions can in turn be abstracted to a topological network
of places and paths. Local metrical models, such as occupancy grids,
of neighborhoods of places and paths can then be built on the
framework of the topological network without their usual problems of
global consistency.
We are extending these ideas in two directions. First, we are
exploring the hypothesis that a similar hierarchy of representations
can be used for acquisition and use of knowledge of visual space,
building from active feature trackers to multiple frames of reference
for spatial relations. Second, we are applying our ideas to the
development of an intelligent wheelchair, intended to serve a driver
with normal cognition but severely impaired mobility and communication
capabilities. The intelligent wheelchair application raises a number
of exciting issues in spatial reasoning about both large-scale and
visual space, multi-modal sensor integration, and mixed-initiative
human-computer interfaces.
Eyal Amir
Last modified: Sun Apr 12 18:31:00 PDT 1998