The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
- which have restricted their widespread adoption in the survey 
and mapping industries. The system will satisfy the demand for 
a mobile mapping system that can compete in both more high 
position accuracy and automatic mapping, which will be 
discussed in the following sections. 
2. HIGH-ACCURACY POSITIONING BY FUSING 
MULTI-SENSOR DATA 
Due to poor navigation sensors, the absolute accuracy of the 
object space points was too poor for many applications - 
especially when compared with competing technologies. So for 
broad utilization in more application, it must increase absolute 
object space accuracies by using more high accuracy hardware 
or using more sophisticated processing techniques. In this 
research, the POS/LV™ of Applanix corp. is utilized as our 
navigation sensor, the power of the navigation system 
consumedly improve the position accuracy of mapping. But in 
some of case such as in poor GPS signal area, it also cannot get 
ideal accuracy. 
As well known, bundle adjustment is efficient method to 
improve accuracy, but it is difficult to do it full automatically 
duo to fallible image matching for tie point. Based on this 
knowledge, this paper presents a robust automatic extraction 
method for tie points. And then, photogrammetry bundle 
adjustment technology is developed to improve and stabilize 
accuracy of positioning. 
2.1 Robust Automatic Extraction of Tie Point 
With the obtained tie points, a bundle adjustment can achieve 
an improved exterior orientation of the camera, which results in 
the final mapping position accuracy. To extract tie points in 
image sequence robustly and precisely is the prerequisite 
condition for Photogrammetry Bundle Adjustment. But, 
automatic and robust extraction of tie points in image is one of 
the most challenging problems in computer vision and digital 
photogrammetry. The difficulty of tie point extraction is image 
matching of these extracted feature points (comers), due to 
easily happened amphibolous matching. The traditional and 
efficient solution of the matching problem is to use rectified 
images, which reduce the complexity of the matching 
algorithms from 2D to ID search. 
But, even if the epipolar geometry is used to image matching, 
the amphibolous matching problem also happens along epipolar 
line. For solving this problem perfectly, the laser 3D point 
clouds are used for image matching to solve that amphibolous 
matching problem. The figure 1 shows how laser 3D points 
facilitate the solution of the image matching problem with any 
no-amphibolous matching. With referring to 3D laser point 
clouds, the extracted feature point can achieve its 3D coordinate 
in mapping frame, then the initial coordinate of that feature 
point in the next image can be computed by projecting 3D 
coordinate to image plane. Finally, just inching adjustment is 
performed by image matching. Because the adjustment is done 
in small area, the amphibolous stereo image matching can be 
solved and matching become more robust. 
Figure 1. Automatic Tie Point Extraction by Supporting of 
Laser Point Cloud 
2.2 Method of Data Fusing Processing for High-Accuracy 
Positioning 
The key step for our high-accuracy positioning is automatic 
accurately tie point extraction by supporting of laser point could, 
which have been described by detail in the above section. As 
the figure 2 shown, the navigation data should be calculated for 
initial position and orientation for laser and camera, so that the 
tie point of image can be extracted automatically with help of 
the initial laser point cloud. 
Figure2. Data Fusion Processing for High-accuracy 
Positioning 
With using accurate extracted tie points, bundle adjustment 
(which is almost exclusively don in close-range 
photogrammetry) is processed for improving accuracy of 
camera position and orientation (X, Y, Z, <t> , a> , k ). The