This paper reviews the state-of-the art in the field of lock-in time-of-flight (ToF) cameras, their advantages, their limitations, the existing calibration methods, and the way they are being used, sometimes in combination with other sensors. Even though lock-in ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight, and reduced power consumption have motivated their increasing usage in several research areas, such as computer graphics, machine vision, and robotics.

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Lock-in Time-of-Flight (ToF) Cameras: A Survey

The most common and well-known principle of 3D image acquisition is stereo vision (SV). This principle of 3D-image acquisition is already known and used for decades in the research community. The advantage of stereo vision to other range measuring devices such as laser scanners, acoustic or radar sensors is that it achieves high resolution and simultaneous acquisition of the entire range image without energy emission or moving parts. Still, the major disadvantage is the limited Field of View (FOV) and the correspondence problem. To enhance the FOV many techniques are researched such as rotating cameras (Kang et al., 1997; Benosman et al., 1996; Krishnan et al., 1996), increasing the number of the cameras (Kawanishi et al., 1998), and the use of a special optic (Liancheng & Feng, 2005; Lin & Bajcsyg, 2003). Also a combination of 1D-laser scanner and a SV system are proposed to overcome the FOV problem (Cheng et al., 2002). However, these systems are expensive and not easy too synchronize. Due to the correspondence problem algorithms need to be improved to give a lower percentage of false matches as well as better accuracy of depth estimates. Performance of algorithm needs to be evaluated over a broad range of image types in order to test their robustness (Dhond & Aggarwal, 1989). In the past years the modality of Time-of-Flight (TOF) imaging became more and more attractive to a growing research community (Schwarte_a et al., 1997; Schwarte_b et al., 1997). Because of the enormous progress in TOF-vision systems, nowadays 3D matrix cameras can be manufactured und be used for many application such as robotic, automotive, industrial, medical and multimedia applications. Due to the increasing demand of safety requirements in the automotive industry it can be assumed that the TOF-camera market will grow and the unit price of these systems in the mass production will drop down to ca. 100 € (Hussmann & Hess, 2006). For all application areas new accurate and fast algorithms for 3D object recognition and classification are needed. As now commercial 3D-TOF cameras are available at a reasonable price the number of research projects is expected to increase significantly. One early example of using a TOF-camera based on the Photonic-Mixer-Devices (PMD)-Technology for 3D object recognition in TOF data sets are presented in (Hess et al., 2003). In this paper the transition from a general model of the system to specific applications such as intelligent O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

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A Performance Review of 3D TOF Vision Systems in Comparison to Stereo Vision Systems

Stereo vision (SV) is widely used and is well known for many 3-D image applications. In this paper, a 3D time-of-flight system will be introduced, which is integrated into a robot vision system. The pros and cons of the system in comparison with SV are discussed, and the basic equations needed to acquire the range information are illustrated. Moreover, object segmentation algorithms working on the range data are presented. Experimental results show that the accuracy and repeatability of such a system is applicable for industrial applications.

Three-Dimensional TOF Robot Vision System

ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited. After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.

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ToF cameras for active vision in robotics

Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies.

/pdf/image-guided-tof-depth-upsampling-a-survey-1n6w3xj431.pdf

Image-guided ToF depth upsampling: a survey

Stereo vision is widely used and well known for many 3D image applications. In this paper a 3D time-of-flight system will be introduced which is integrated into a robot vision system. The pro and cons of the system in comparison to stereo vision are discussed and the basic equations needed for acquiring the range information are derived. Moreover object segmentation algorithms working on the range data are presented and experimental results verify the function of the proposed robot vision system.

T. Liepert

Papers

Robot Vision System based on a 3D-TOF Camera