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

1.3. What has already been achieved? 4
would probably give the most general world representation. In fact, certain tasks would require
the use of vision sensors. Imagine a mobile robot with the task to find a certain object, lets say
a coffee cup, for its user. The task of detecting the coffee cup can certainly not get achieved
with ranging devices solely. Although one can think about detecting a cup by its 3D shape with
a laser range finder this method cannot distinguish between similar cups differing only in the
color. Such a task requires a vision sensor, and thus as vision is already onboard it is tempting
to use it for navigation and localization too.
1.3 What has already been achieved?
The use of vision sensors for mobile robot localization has not yet reached an as elaborate state as
the use of laser range finders. Mobile robots equipped with laser range finders already navigate
safely in unknown and people crowded environments [104] and are able to build large and
accurate maps [105]. But lets discuss what has been achieved using visual sensors in odometry,
localization and map building.
In the absence of a map or within a featureless environment visual odometry can be used to
compute the path a robot went and thus the actual position can be computed from the travelled
path. For visual odometry point features are tracked from frame to frame and with a structurefrom-motion
approach the robots movement for each frame can be computed. The estimation
has to be very accurate, because the final position is computed incrementally from all small
movements. And even small inaccuracies might result into big deviations. The capabilities of
the current state-of-the-art in visual odometry has been shown impressively in NASA’s Mars
Exploration Rovers ”Spirit” and ”Opportunity” [86]. The slippy surface did not allow for an
accurate wheel odometry computation and laser range finders could not be used in the open
outdoor environment. However, a fully autonomous visual based navigation was still not possible
and the rovers where controlled by human operators in the end to compensate for errors of the
visual localization system.
The current state-of-the-art in visual localization is defined by vSlam [56] and the method
described in [96]. Both systems are SLAM-approaches based on SIFT-landmarks [67] and show
very similar performances. The approaches allow map building in indoor environments of the
size of a room up to small flats. The robot will explore the environment autonomously and
create a map of 3D point landmarks. After the map creation is finished the robot can perform
global localization and path planning tasks. The achieved localization accuracy is about 10-15
cm at average. For vSlam the robot has to be equipped with a single camera only. The 3D
reconstruction of the landmarks is done with a structure-from-motion approach. The other
system uses a stereo setup on the robot for 3D reconstruction. A main limitation of the systems
is the size of the maintained environment map. For bigger than room-size environments the
map will be too big to be handled in real-time.
The last example deals with the automatic map building of large scale environments and
outdoor environments. The system proposed in [10] is capable of mapping a path of the length
of several kilometers accurately. The large scale map is composed of connected metric sub-maps.
The sub-maps contain 3D line features. The system allows loop closing by matching the 3D lines
from the current reconstruction to the 3D lines of the sub-maps. A global optimization ensures
the high accuracy of the map. However, the map features are purely geometric and the system
will get difficulties in buildings with highly similar structures. Moreover the method does not
allow global localization in general. The map in the presented form cannot be used for robot
navigation and localization.