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3D cameras. 3D computer vision of wide scope

: May, S.; Pervölz, K.; Surmann, H.

Fulltext urn:nbn:de:0011-n-815196 (313 KByte PDF)
MD5 Fingerprint: 83cccc68ce3fadad4dc46b413d192898
Created on: 11.9.2008

Obinata, G.:
Vision systems. Applications
Vienna: I-TECH, 2007
ISBN: 978-3-902613-01-1
Book Article, Electronic Publication
Fraunhofer IAIS ()
3D camera; vision system

The human visual sense is the one among all other senses that gathers most information we receive. Evolution has optimized our visual system to negotiate one's way in three dimensions even through cluttered environments. For perceiving 3D information, the human brain uses three important principles: stereo vision, motion parallax and a-priori knowledge about the perspective appearance of objects in dependency of their distance. These tasks pose a challenge to computer vision since decades. Today the most common techniques for 3D sensing are CCD- or CMOS-camera, laser scanner or 3D time-of-flight camera based. Even though evolution has shown predominance for passive stereo vision systems, three additional problems are remaining for 3D perception compared with the two mentioned active vision systems above. First, the computation needs a great deal of performance, since the correlation of two images from a different point of view has to be found. Second, distances to structureless surfaces cannot be measured, if the perspective projection of the object is larger than the camera’s field of view. This problem is often called aperture problem. Finally, a passive visual sensor has to cope with shadowing effects and changes in illumination over time. That is why for mapping purposes mostly active vision systems like laser scanners are used , e.g. [Thrun et al., 2000], [Wulf & Wagner, 2003], [Surmann et al., 2003]. But these approaches are usually not applicable to tasks considering environment dynamics. Due to this restriction, 3D cameras [CSEM SA, 2007], [PMDTec, 2007] have attracted attention since their invention nearly a decade ago. Distance measurements are also based on a time-of-flight principle but with an important difference. Instead of sampling laser beams serially to acquire distance data point-wise, the entire scene is measured in parallel with a modulated surface. This principle allows for higher frame rates and thus enables the consideration of environment dynamics. The first part of this chapter discusses the physical principles of 3D sensors, which are commonly used in the robotics community for typical problems like mapping and navigation. The second part concentrates on 3D cameras, their assets, drawbacks and perspectives. Based on these examining parts, some solutions are discussed that handle common problems occurring in dynamic environments with changing lighting conditions. Finally, it is shown in the last part of this chapter how 3D cameras can be applied to mapping, object localization and feature tracking tasks.