Towards an alternative GPS sensor in dense urban environment from visual memory

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 first 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 first extracting key frames from the learning sequence. Then the epipolar geometry and camera motion are computed between key frames. Additionally, a hierachical bundle adjustment is used to refine the reconstruction. The map stored for the localization include the position odf 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.