Cobb%MIT-OZ@MIT-MC.ARPA (04/18/84)
[Forwarded from the MIT bboard by SASW@MIT-MC.]
W. ERIC L. GRIMSON
Local Constraints in
Model Based Recognition and Localization
From Sparse Data
April 23, 1984 4:00PM
NE43-8th floor playroom
A central characteristic of advanced applications in robotics is the
presence of significant uncertainty about the identities and attitudes
of objects in the workspace of a robot. The recognition and
localization of an object, from among a set of models, using sparse,
noisy sensory data can be cast as the search for a consistent matching
of the data elements to model elements. To minimize the computation,
local constraints are needed to limit the portions of the search space
that must be explicitly explored.
We derive a set of local geometric constraints for both the three
degree of freedom problem of isolated objects in stable positions, and
the general six degree of freedom problem of an object arbitrarily
oriented in space. We establish that the constraints are complete for
the case of three degrees of freedom, but not for six. We then show
by combinatorial analysis that the constraints are generally very
effective in restricting the search space and provide estimates for
the number of sparse data points needed to uniquely identify and
isolate the object. These results are supported by simulations of the
recognition technique under a variety of conditions that also
demonstrate its graceful degradation in the presence of noise. We
also discuss examples of the technique applied to real data from
several sensory modalities including laser ranging, sonar, and grey
level imaging.
Refreshments: 3:45PM
Host: Professor Patrick H. Winston