BMVC 2004, Kingston, 7th-9th Sept, 2004

Towards an alternative GPS sensor in dense urban environment from visual memory
E. Royer, M. Lhuillier, M. Dhome and T. Chateau (Universit
Blaise-Pascal, France)

In this paper we present a method for computing the localization of a
mobile robot with reference to a learning video sequence. The robot is rst
guided on a path by a human, while the camera records a monocular learning
sequence. Then the computer builds a map of the environment. This is done
by rst extracting key frames from the learning sequence. Then the epipolar
geometry and camera motion are computed between key frames. Additionally
a hierarchical bundle adjustment is used to rene the reconstruction. The
map stored for the localization include the position of the camera associated
with each key frame as well as a set of interest points detected in the images
and reconstructed in 3D. Using this map it is possible to compute the
localization of the robot in real time during the automatic driving phase.
(pdf article)