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262 J. Gaspar et al.

Fig. 18. Interactive modelling based on co-planarity and co-linearity properties
using a single omnidirectional image. (Top) Original image with superposed points
and lines localised by the user. Planes orthogonal to the x, y and z axis are shown in
light gray, white, and dark gray respectively. (Table) The numbers are the indexes
shown on the image. (Below) Reconstruction result and view of the textured mapped
3D model

Figure 18 shows the resulting texture-mapped reconstruction. This result
shows the effectiveness of omnidirectional imaging to visualize the immediate
vicinity of the sensor. It is interesting to note that just a few omnidirectional
images are sufficient for building the 3D model (the example shown utilized a
single image), as opposed to a larger number of “normal” images that would
be required to reconstruct the same scene [50, 79].

4.2 Human Robot Interface based on 3D World Models

Now that we have the 3D scene model, we can build the Human Robot inter-
face. In addition to the local headings or poses, the 3D model allows us to spec-
ify complete missions. The human operator selects the start and end locations
in the model, and can indicate points of interest for the robot to undertake
specific tasks. See Fig. 19.
Given that the targets are specified on interactive models, i.e. models built
and used on the user side, they need to be translated as tasks that the robot
understands. The translation depends on the local world models and navi-
gation sequences the robot has in its database. Most of the world that the
robot knows is in the form of a topological map. In this case the targets are
images that the robot has in its image database. The images used to build