Laser stripe peak detector for 3D scanners. A FIR filter approach

Forest, Josep; Salví, Joaquím; Cabruja, E.; Pous, Carles

doi:10.1109/icpr.2004.1334612

Cited by 93 publications

(52 citation statements)

References 7 publications

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“…17 However, when more planes are projected, the derived curve of the profile is high influenced by the neighborhood. In some situations, the derived curve does not cross to zero at the maximum value of the intensity profile.…”

Section: Points In the Laser Profilementioning

confidence: 99%

A refined range image registration technique for multi-stripe laser scanner

et al. 2006

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Nowadays, visual inspection is very important in the quality control for many industrial applications. However, the complexity of most 3D objects constrains the registration of range images; a complete surface is required to compare the acquired surface to the model. Range finders are very used to digitalize free form shape objects with large resolutions. Moreover, one single view is not enough to reconstruct the whole surface due to occlusions, shadows, etc. In these situations, the motion between reconstructed partial views are required to integrate all surfaces in a single model. However, the use of positioning systems is not always available or adequate due mainly to the size of the objects or the environmental conditions imposed by the precise mechanics which suffer from vibrations present in the industry. In order to solve this problem, a 3D hand sensor is developed to reconstruct 3D objects that let us to compare them with respect the original one.

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Section: Points In the Laser Profilementioning

confidence: 99%

A refined range image registration technique for multi-stripe laser scanner

et al. 2006

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“…Fisher and Naidu [13] have presented a comparison of five methods, that is, Gaussian approximation, center of mass, linear interpolation, parabolic estimator, and Blais and Rioux method for detection of laser line to subpixel accuracy assuming that the spread of laser line is not random but rather conforms to some kind of Gaussian distribution. Forest et al [14] named three types of noise sources, that is, electrical noise, quantization noise, and speckle noise, that jointly produce constructive and destructive interferences within the laser line and give it a granular appearance. They suggested a low pass FIR filter with right cut-off frequency that should be used before finding the peaks in laser line.…”

Section: Related Workmentioning

confidence: 99%

High Precision Laser Scanning of Metallic Surfaces

Amir

Thörnberg

2017

International Journal of Optics

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Speckle noise, dynamic range of light intensity, and spurious reflections are major challenges when laser scanners are used for 3D surface acquisition. In this work, a series of image processing operations, that is, Spatial Compound Imaging, High Dynamic Range Extension, Gray Level Transformation, and Most Similar Nearest Neighbor are proposed to overcome the challenges coming from the target surface. A prototype scanner for metallic surfaces is designed to explore combinations of these image processing operations. The main goal is to find the combination of operations that will lead to the highest possible robustness and measurement precision at the lowest possible computational load. Inspection of metallic tools where the surface of its edge must be measured at micrometer precision is our test case. Precision of heights measured without using the proposed image processing is firstly analyzed to be ±7.6 m at 68% confidence level. The best achieved height precision was ±4.2 m. This improvement comes at 24 times longer processing time and five times longer scanning time. Dynamic range extension of the image capture improves robustness since the numbers of saturated or underexposed pixels are substantially reduced. Using a high dynamic range (HDR) camera offers a compromise between processing time, robustness, and precision.

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“…The first two are inherent to the image sensor and the last is caused by the nature of laser light. Authors in [25] present the FIR filter based filtering method which successfully deals with artefacts caused by scanning different surfaces with different noise levels.…”

Section: Laser Line Scanningmentioning

confidence: 99%