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

7.1. Map building 111
point landmark. In fact, already a single plane match allows pose estimation while this is
not possible with a single 3D point landmark.
Additional geometric constraints: Landmarks which are located on one and the same 3D
plane are connected by geometric constraints. Plane projective relations are much more
restrictive than general projective relations. A planar homography can be used very efficiently
to verify feature matches geometrically.
Feature reduction: By selecting only landmarks located on 3D planes the number of stored
features in the map is reduced significantly. The map uses less memory and the computation
time for feature matching of course depends on the number of features. It also
increases the robustness and reliability. Non-planar features may change in appearance
more significantly than planar features under viewpoint changes. Such landmarks are the
reason for ambiguities in feature matching, and mis-matches will occur more frequently
which cause problems in pose estimation.
Easier matching of planar landmarks: State-of-the-art wide-baseline methods assume that
landmarks undergo a planar projective transformation under viewpoint change. Approximating
the projective transformation by an affine transformation to create viewpoint
normalized descriptors are the currently most advanced matching methods. Landmarks
located on 3D corners strongly violate the just mentioned assumption. Such features would
cause troubles for matching algorithms and should not be stored as landmarks in the map.
Increased accuracy: The accuracy of the 3D reconstruction can be increased with plane information.
3D point reconstructions are coupled by geometric constraints and the 3D
coordinates can be optimized to be arranged exactly as a plane.
In the following a batch method for map building is presented. Input for the method is an
image sequence acquired from a mobile robot equipped with a single perspective camera. The
camera needs to be calibrated beforehand. Structure-from-motion algorithms and wide-baseline
stereo methods are applied to build the piece-wise planar world representation. The created
map can then be used for purely vision based global localization. A mobile robot equipped with
a single perspective camera can estimate its pose in respect to the world map from a single
camera image.
The localization approach to be presented is in analogy to [56] as it computes the robot
pose from 3D-2D point correspondences. The novelty is the use of small planar patches as
3D landmarks and that the pose can be computed from a single landmark correspondence.
This allows to do localization under extreme conditions, where other methods which require
usually a high number of correspondences would normally fail. The novel localization approach
is presented in the second part of this chapter.
7.1 Map building
The world is represented as a network of linked metric sub-maps (see Figure 7.1 for illustration).
Each sub-map has its own local coordinate system and each link between two sub-maps
represents a rigid transformation (containing rotation, translation and scaling) connecting both
local coordinate system. Thus it is possible to express a position within a specific sub-map
from each local coordinate system. Furthermore each sub-map contains the transformation into