PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

1.2. Why vision? 3
resentation. A possible classification of the different paradigms according to [65] might be:
topological, metric and appearance based.
Topological maps: Topological maps represent the environment as connected graphs. The
nodes of the graph are possible places, e.g. rooms. Connected nodes therefore represent
places which are located close by and are reachable for the robot. Navigation and path
planning in such a map can be difficult. The robot only gets the information which places
need to be traversed to get to its goal, but in the absence of metric information no direction
information or distance information can be given.
Metric maps: In a metric map the single map elements are spatially organized, that means
the position of a map element (landmark) is known in a common world coordinate frame.
Metric maps can differ widely by the used landmarks. One possible metric world representation
partitions the known world by a grid into discrete cells [81]. For each cell position
it is stored if the position is occupied by an object (e.g. wall, tables, etc.) or if it is free.
Such a map is often called occupancy grid, and basically represents the 2D floor plan of
an environment. As the size of each grid cell is, known metric information is available and
allows distance computations and metric path planning. Another possibility is to represent
the world by geometric features which are positioned in 3D [96]. Such features can be 3D
points, 3D lines, etc. Localization in such a world can be done by triangulation. The main
difficulty however is to find the correspondences between the features in the map and the
features detected by the current sensor readings.
Appearance-based maps: In such an approach the world is represented by raw sensor data.
The map is simply a collection of all previously acquired sensor readings [51]. Guided
navigation and localization is difficult. But the main problem however is the scalability of
the approach. Simply storing all the sensor readings is very memory consuming and poses
a big problem for large scale maps.
In contrast to this classification, combinations of the different approaches are also proposed
in the literature. In [103] metric grid-maps are connected by a topological approach on top to
generate the world representation.
1.2 Why vision?
Most of the maps described in the previous section do not depend on a specific kind of sensors.
In fact, research is done with a variety of different sensors. The prominent sensors for robot
localization are wheel encoders (odometry), inertial sensors, sonar, infrared, laser range finders
and of course vision sensors. Each sensor type shows different advantages and disadvantages, a
list may be found in [65]. Wheel encoders and inertial sensors provide direct information about
the path of the robot. Sonar, infrared sensors and laser range finders are ranging devices. They
provide the robot with more or less (depending of the type of the sensor) accurate distances to
objects in the vicinity of the robot. However, they only provide distance information. Compared
to these sensors a vision sensor seems to be the most powerful one. A vision sensor can provide
odometry information as described in [84]. A vision sensor can also act as ranging device, either
as a stereo setup (demonstrated in [80]) or with a structure-from-motion approach [106]. In
addition a vision sensor allows to record the appearance of the world surrounding the robot.
The visual appearance of landmarks can now be associated to range information. A vision sensor