a mobile robot starts at an unknown position in an unknown
environment and incrementally builds a 3D model of the visited area.
Finding an effective way to accomplish this task is deeply related
to some design choices:
- robot sensors
- environment representation.
- Simultaneous Localization and Mapping (SLAM)
is addressed by a mathematical formalization of the dynamic
system formed by the robot location and the location of all
the features in the map and by computing the status of these
system (for example by using an Extended Kalman Filter).
The need of considering all the relationships between
the features in the map makes the use of these approaches
computationally intractable in many real applications.
- Camera Motion Estimation is addressed by using a
mathematical formalization of the camera device and
solving a over-constrained linear system with features extracted
from images (e.g. lines and corners from images, etc.).
Motion estimation from two or three images at a time is affected
by error accumulation over long sequences.
point/feature correspondences (i.e. data association).
We have investigated a new approach in which we focus our attention at the
solution of both the motion estimation and the data association
problems at the same time.
The general idea is to divide the process in two steps:
- rough motion prediction by odometry and stereo
- fine data matching and motion estimation by a local search
in the robot's pose space.
- Environment 3D modelling by using Hough Localization and Image Correlation,
under the assumption of planar motion
and planar surfaces.
- Camera motion estimation and 3D object modelling by using Large Baseline