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

7.1. Map building 116
distance is used. Two features correspond if
d 01
d 02
< t (7.6)
where d 01 is the Euclidean distance between the query feature and the nearest feature point from
D ′ and d 02 is the Euclidean distance to the second closest feature. t is a distance threshold.
Good results can be achieved with t=0.8. The distance measure has been suggested by Lowe [67]
for SIFT feature matching. The such established feature correspondences can now be used for
estimating the camera poses. As already mentioned we assume calibrated cameras, i.e. the
calibration matrix K is know. Thus we can estimate the essential matrix which encodes the
camera positions of the two viewpoints. Essential matrix estimation is performed using the 5-
point algorithm of Nister [83] within a standard RANSAC scheme [28]. The essential matrix E
can be decomposed into two camera matrices P, P ′ where P is the canonical camera matrix and
P ′ defines the second camera position in the local canonical coordinate frame (see equation 7.7
and 7.8).
⎛
⎞
1 0 0 0
P = ⎝ 0 1 0 0 ⎠ (7.7)
0 0 1 0
P ′ = [R|t] (7.8)
R is a 3 × 3 rotation matrix and t is a translation 3-vector defining the baseline of the stereo
case. P, P ′ are input parameters for the subsequent plane segmentation and reconstruction step.
The algorithm requires also initial guesses for small planar regions in the images I, I ′ as input
parameters with the according inter-image homographies. For that MSER regions are detected
in I and I ′ . Region matching is performed (as described in Section 6.1) which returns point
correspondences within each interest region and the according homography transform. This
constitutes the initial guesses for the plane segmentation algorithm. Plane segmentation and
reconstruction is now performed yielding the following map components:
• An index image I.
• A set of detected and reconstructed planes Π. Each plane is represented by a 6-vector
giving the full 3D parameters in the local coordinate frame.
• Covariances for each plane giving an uncertainty measure for the reconstruction accuracy.
The structure computation will be completed by a post-processing step. Planes which are
extended behind the camera planes are removed. This consistency check deletes incorrect reconstructed
image parts resulting in higher robustness. The different steps of structure reconstruction
are illustrated in Figure 7.3.
7.1.4 Landmark extraction
Up to now the sub-map is still missing essential components, the landmarks. Closing this gap
is the goal of the next step. Landmark appearance has to be connected to 3D information and
incorporated into the sub-map. In the following we describe an approach using MSER interest
regions as landmarks. However, the definition of the sub-map is general enough to allow the use
of any other kind of interest regions. In the following the necessary steps are outlined: