(Georgia Institute of Technology, 2007-10)
Folkesson, John; Jensfelt, Patric; Christensen, Henrik I.
In this paper, a new feature representation for simultaneous
localization and mapping (SLAM) is discussed. The
representation addresses feature symmetries and constraints
explicitly to make the basic model numerically robust. In previous
SLAM work, complete initialization of features is typically performed
prior to introduction of a new feature into the map. This
results in delayed use of new data. To allow early use of sensory
data, the new feature representation addresses the use of features
that initially have been partially observed. This is achieved by explicitly
modelling the subspace of a feature that has been observed.
In addition to accounting for the special properties of each feature
type, the commonalities can be exploited in the new representation
to create a feature framework that allows for interchanging of
SLAM algorithms, sensor and features. Experimental results are
presented using a low-cost web-cam, a laser range scanner, and
combinations thereof.
(Georgia Institute of Technology, 2006-03)
Jensfelt, Patric; Folkesson, John; Kragic, Danica; Christensen, Henrik I.
Simultaneous localization and mapping (SLAM) is an important research area in
robotics. Lately, systems that use a single bearing-only sensors have received significant attention
and the use of visual sensors have been strongly advocated. In this paper, we present a
framework for 3D bearing only SLAM using a single camera. We concentrate on image feature
selection in order to achieve precise localization and thus good reconstruction in 3D. In
addition, we demonstrate how these features can be managed to provide real-time performance
and fast matching to detect loop-closing situations. The proposed vision system has been combined
with an extended Kalman Filter (EKF) based SLAM method. A number of experiments
have been performed in indoor environments which demonstrate the validity and effectiveness
of the approach. We also show how the SLAM generated map can be used for robot localization.
The use of vision features which are distinguishable allows a straightforward solution to
the "kidnapped-robot" scenario.