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

2.5. Summary 17
Component analysis to compute a representative feature vector. Using these landmarks, map
building is described as an off-line approach. For map building a robot equipped with a camera
and a laser range finder is steered through the environment. Planar landmarks are detected
by the previously described method. For each landmark a 3D reconstruction of the contour
will be performed from two successive frames. The reconstructed landmark will be put into the
global coordinate system by using the robot pose information of the laser range finder. Robot
localization then works by matching planar landmarks detected in the current view with the
map landmarks. For a matched landmark the four corner points are used to create 3D-2D
point correspondences. The pose of the robot is then computed from the four 3D-2D point
correspondences using the planar P4P method and a subsequent iterative refinement [45]. A
key concept of this approach is the visibility map for localization. It is assumed that a pathplanning
process defines a trajectory in the world coordinate frame. According to this path the
best suited landmarks are selected for different sections of the path. Localization is performed in
this approach from a single landmark. The active camera is used to keep the landmarks selected
from the visibility map in the field-of-view. The quadrangular landmarks however impose a very
strong restriction upon the potential landmarks. Even for indoor environments this is a big
limitation. And the localization algorithm relies on the four corner points. A single occluded
corner point renders the whole landmark invalid. For practical applications these constraint will
be to rigid.
A different SLAM approach using plane features has been proposed by Molton et al. [77].
The pose is computed by image alignment between learned and detected landmarks.
2.5 Summary
The focus of the presented state-of-the-art in the last sections was drawn to methods which allow
the construction of a world map and allow global localization therein. Both, SLAM approaches
and batch approaches were enlisted. A SLAM approach allows incremental map building during
operation. In a batch approach a map has to be created prior robot operation. This does not
mean, that the map building could not be automatically. A mobile robot navigating with a
laser range finder could traverse the environment and acquire image data. The world map could
then be constructed off-line from the image data. Afterwards this map could be used by other
robots only equipped with a digital camera for localization and navigation. This makes sense as
digital cameras are much cheaper than a laser range finder and the robots which operate later
on only need a digital camera. In general a SLAM approach is more challenging to develop, but
for most cases it would be possible to extend existing batch versions to full SLAM approaches.
The approaches proposed by Se et al. [96], Karlsson et al. [56] and Davison et al. [21] are SLAM
approaches. The others are batch approaches. The approach from Kosaka [59] even requires a
manually constructed CAD-model of the world. All of the presented SLAM approaches use a
metric map containing 3D point features. Extraction and reconstruction of point landmarks can
be done very fast. Other landmarks as lines, planes or vanishing points require more complex
detection and reconstruction methods and might be not applicable for a real-time updating of
the landmarks as required in a SLAM framework. Until know the influence of the map features
for navigation and path planning has been neglected. However, it is worth to be discussed.
For path planning the robot has to know which locations are obstructed and which are free.
Clearly, this information should be provided from the world representation. The described
SLAM approaches construct sparse world representations consisting of distinct point features
only. A path planning algorithm does not know if the space between the landmarks is occupied