Global Positioning System Reference
In-Depth Information
5.2.2 Object extraction
Figure 14 shows the point cloud acquired by the laser scanner, which is geo referenced with
hybrid positioning data and contains not only traffic signs/signals but also the
surroundings (vegetation and buildings) beyond the road. Object segmentation and feature
extraction of important objects, such as traffic signs/signals, are conducted. This is most
common especially after most redundant range points have been detached from the point
cloud after boundary extraction. Range points represent the geographic information of all
the objects in the form of 3D discrete coordinates, which have no description of attribute and
there is no topological relation among the data points. However, spatial features exist which
can be used for object segmentation. It is clear that the traffic sign/signal has a strong linear
feature after being projected onto the horizontal plane, while points belonging to other
spatial objects (such as trees) are scattered on the horizontal plane without a dominant
direction. This spatial feature can be utilized for segmentation.
Fig. 14. 3D point cloud data from vehicle based system
6. Validation
In this mobile mapping system, multiple sensors are integrated, thus making it difficult to
point out the origins of errors for positioning. Therefore, accuracy of positioning is assessed
by an ordinary survey method, the result of which is compared with a digital surface model
and control points from the oriented images; the accuracy is 3 to 10 cm, as shown in Table. 3.
Those control points are considered as true values and selected feature points such as object
corners, which can recognize both images and a digital surface model (DSM). As a result,
the average error of the digital surface model is approximately 10 to 30 cm, as shown in
Table. 5.
For this validation, DSM has been reconstructed and geo-referenced by using a hybrid
position. The laser range data are acquired 50 m away from the object and the scan angler
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