Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM) are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tieand/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images' relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.
A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from/to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.978-1-4577-0891-6/11/$26.00 ©2011 IEEE
ABSTRACT:3D surface models with high resolution and high accuracy are of great value in many applications, especially if these models are true to scale. As a promising alternative to active scanners (light section, structured light, laser scanners, etc.) new photogrammetric approaches are coming up. They use modern structure from motion (SfM) techniques, using the camera as main sensor. Unfortunately, the accuracy and resolution achievable with the available tools is very limited. When reconstructing large objects with high resolution an unacceptably high laborious effort is another problem. This paper shows an approach to overcome these limitations. It combines the strengths of modern surface reconstruction techniques from the remote sensing sector with novel SfM technologies, resulting in accurate 3D models of indoor and outdoor scenes. Starting with the image acquisition all particular steps to a final 3D model are explained. Finally the results of the evaluation of the approach at different indoor scenes are presented.
<p><strong>Abstract.</strong> Geometric camera calibration is a mandatory prerequisite for many applications in computer vision and photogrammetry. Especially when requiring an accurate camera model the effort for calibration can increase dramatically. For the calibration of the stereo-camera used for optical navigation a new chessboard based approach is presented. It is derived from different parts of existing approaches which, taken separately, are not able to meet the requirements. Moreover, the approach adds one novel main feature: It is able to detect all visible chessboard fields with the help of one or more fiducial markers simply sticked on a chessboard (AprilTags). This allows a robust detection of one or more chessboards in a scene, even from extreme perspectives. Except for the acquisition of the calibration images the presented approach enables a fully automatic calibration. Together with the parameters of the interior and relative orientation the full covariance matrix of all model parameters is calculated and provided, allowing a consistent error propagation in the whole processing chain of the imaging system. Even though the main use case for the approach is a stereo camera system it can be used for a multi-camera system with any number of cameras mounted on a rigid frame.</p>
Since 2010 the German Aerospace Center (DLR) is working on the project ATON (Autonomous Terrain-based Optical Navigation). Its objective is the development of technologies which allow autonomous navigation of spacecraft in orbit around and during landing on celestial bodies like the Moon, planets, asteroids and comets. The project developed different image processing techniques and optical navigation methods as well as sensor data fusion. The setup-which is applicable to many exploration missions-consists of an inertial measurement unit (IMU), a laser altimeter, a star tracker and one or multiple navigation cameras. In the past years, several milestones have been achieved. It started with the setup of a simulation environment including the detailed simulation of camera images. This was continued by hardware-in-the-loop tests in the Testbed for Robotic Optical Navigation where images were generated by real cameras in a simulated downscaled lunar landing scene. Data was recorded
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