Techniques For Real-Time, 3D, Feature Extraction Using Range Information

Active, optical range imaging systems collect three-dimensional coordinate data from object surfaces. These systems can be useful in a wide variety of automation applications, including shape acquisition, bin picking, assembly, inspection, gauging, robot navigation, medical diagnosis, cartography, and military tasks. The range-imaging sensors in such systems are unique imaging devices in that the image data points explicitly represent scene surface geometry in a sampled form. At least six different optical principles have been used to actively obtain range images: (1) radar, (2) triangulation, (3) moire, (4) holographic interferometry, (5) lens focusing, and (6) diffraction. The relative capabilities of different sensors and sensing methods are evaluated using a figure of merit based on range accuracy, depth of field, and image acquisition time.

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“…The ERIM ITA sensor is programmable as described in Svetkoff et al [1984]. The depth offield can change from 150 to 900 mm.…”

Section: Amplitude Modulationmentioning

confidence: 99%

Active Optical Range Imaging Sensors

Besl¹

1989

Springer Series in Perception Engineering

129

117

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“…Traditional techniques for 3-D shape measurement include point triangulation, structured illumination, laser radar, stereo vision, Moiré, fringe projection, etc. [1][2][3][4][5][6][7][8][9][10][11][12][13] These techniques vary in their resolutions and sampling speeds, each being suitable for certain types of applications. Recently, some new fringe projection techniques 14 -18 have been developed that take advantage of the new digital video projection technology based on digital micromirror devices ͑DMDs͒.…”

Section: Introductionmentioning

confidence: 99%

Calibration of a three-dimensional shape measurement system

Huang

et al. 2003

Opt. Eng

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A 3-D surface shape measurement system based on a digital fringe projection and phase shifting technique is described. In this system, three phase-shifted fringe patterns and a centerline pattern are used to determine the absolute phase map of the object. This phase map is then converted to the x, y, and z coordinates of the object surface by a conversion algorithm. To determine the accurate values of the system parameters as required by the conversion algorithm, a two-step calibration procedure was developed. The parameters were first measured to determine their approximate values, then a calibration plate was measured by the system at various positions, and an iteration algorithm used to estimate the system parameters. Measurement results of several objects are presented. The standard deviation of the measurement error was found to be 0.23 mm.

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“…• (Horn, 1968;Jarvis, 1976;Krotkov and Martin, 1986) " 25 xi (Yakimovski and Cunningham, 1978;Levine et al, 1973 (Lewis and Johnston, 1977;Nitzan et al, 1981;Moring et al, 1987;Svetkoff et al, 1984 Worst-case complexity of graph construction process (1).…”

Section: Claim Of Originalitymentioning

confidence: 99%

“…2.1.3.2 Range from Time-of-Flight (Lewis and Johnston, 1977;Nitzan et al, 1981;Moring et al, 1987;Svetkoff et al, 1984) The idea of time-of-f1ight ranging is simple: one directs a ray of light onto the scene, and measures the elapsed time until the signal returns. Since the emitter and the receiver are coaxial, this method is immune to the missing part problem of triangulationbased techniques.…”

Section: Irmentioning

confidence: 99%

Three-dimensional model construction from multiple sensor viewpoints

Aubry

Hayward

Proceedings of 1995 IEEE International Conference on Robotics and Automation

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Techniques For Real-Time, 3D, Feature Extraction Using Range Information

Cited by 19 publications

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Active Optical Range Imaging Sensors

Active Optical Range Imaging Sensors

Calibration of a three-dimensional shape measurement system

Three-dimensional model construction from multiple sensor viewpoints

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