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

8.3. Localization experiments 145
8.3.2 Path reconstruction
The main application for global localization is to compute an initial robot position to initialize
a probabilistic SLAM framework, e.g. [78]. The robot position is then usually maintained by an
extended Kalman filter. The Kalman filter will include the knowledge of previous robot positions
and also previous speed and heading estimates. Propagating this values probabilistically will
result in a smooth reconstruction of the traversed path.
In absence of a SLAM framework the traversed path has to be reconstructed by global localization
solely. Each position estimate is then computed independently. Figure 8.12 shows a
part of the robots path through the ”Office” environment reconstructed by global localization.
The path consists of 204 independent pose estimated. The pose estimates are marked with
black dots, together forming the traversed path. The red dots mark gross outliers. Table 8.4
summarizes the corresponding numbers. From 204 total pose estimates 21 showed a large deviation
from the original path (from laser localization), thus they are marked as gross outliers.
Such gross outliers would be detected by the Kalman filtering. The average epipolar distance,
computed as a measure of accuracy, is 1.45 pixel.
Figure 8.13 shows the reconstruction of a robots path in the ”Hallway” environment. In fact,
three different sections of a robots path are shown. In total 124 positions have been computed.
The corresponding paths are drawn as black dots. For this scenario no laser ground truth is
available, thus outlier detection did not apply.
#frames (positions) #correct (%) #bad estimates (%) avg. epipolar distance
(std. dev.) [pixel]
Office 204 183 (89.7%) 21 (10.3%) 1.45 (1.0)
Hallway 124 - - 1.49 (0.74)
Table 8.4: Number of correct pose estimates and bad estimates for the path reconstruction
experiment. The accuracy of the pose estimates are expressed by the epipolar distance.