IEEE Robotics & Automation Magazine - September 2013 - 57

In Figure 6, the results of the analysis of the ToF data for five
experiments (1-5) obtained for the far viewing position are
shown. In general, depth segmentation delivered sufficiently
good results to identify targets of interest. Except for experiment
3 [Figure 6(c)], for which the segmentation failed, targets of sufficient validity could always be found. The values of the validity
and graspability measure are summarized in Table 1. Targets
selected during the experiments are labeled with a unique number in the figures and the table. The computed grasping points
are indicated with a star-shaped symbol in the figures.
Based on the selected target, a close view of the target can
be planned using its 3-D pose. After moving to the close
position, the newly acquired data is analyzed. As can be seen
in Figure 7, the segmentation improves in the close view
compared with the general view, and in all cases except
experiment 3, target leaves can be confirmed indicated by a

sufficiently large validity ( v 2 0.3 ). However, in experiment
5, the validity decreased by 0.14, which is nevertheless still in
the error margin of the validity computation. The validity
measure can be impaired by many factors, e.g., shape differences of the real leaf compared with the model leaf, nonoptimal solutions encountered by the fitting procedure, the segmentation errors distorting the boundaries.
Since the contour models provide just a rough approximation, and the validity estimation is afflicted with some
error as well (see the previous section), validities are
expected to increase from the far view to the close view
only if the change in the viewing angle is large (>30°).
Although the graspabilities close to zero indicate that a
grasp is executable, the segmentation errors or noise in the
data can cause the graspability value to deviate slightly from
zero, e.g., experiment 4.

2
(a)

3

(b)

7
(c)

5

(d)

6

(e)
Figure 7. The segmentation and target selection results for ToF data acquired from a close viewing position relative to the plant for
the five different experiments. (a)-(e) correspond to experiments 1-5, respectively. Depth is color coded with increasing depth coded
from red to blue (second and fourth column). The grasp points of selected and labeled target segments are marked with a red star.
The results for the close view of target 1 are shown in Figure 3. Columns represent (from left): intensity (PMD) close view, depth
(PMD), segmentation, fitted surfaces, and segment validity.

september 2013

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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