Stereo vision in LHD automation

Whitehorn, Mark; Vincent, Tyrone L.; Debrunner, C.; Steele, John P.

doi:10.1109/tia.2002.807245

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…In his study, he eliminated noise by acquiring data on the targeted structures within the threshold by estimating the linear equation of the structures in three-dimensional space. Whitehorn et al (2003) examined an underground mining site in three dimensions using an LHD (Load-Haul-Dump) device equipped with stereo-vision equipment and lighting. In their research, a ZSSD (Zero-mean Sum of Squared Differences) algorithm was used in the stereo matching process with Gaussian and median filters applied to reduce noise.…”

Section: Previous Research On Noise Elimination Algorithmsmentioning

confidence: 99%

“…On the other hand, the noise was divided into only two patterns, such as gauss noise and peak noise, and the triangle mesh modeling conversion time was also required considering the earthwork site, which makes the application problematic. Whitehorn et al (2003) used preprocessors, such as a Gaussian filter and median filter, to reduce noise in the 3D stereo matching phase. This was effective in reducing the overall image noise level but there were some limitations in treating noise.…”

Section: Review Of the Problems Of Previous Studies On Noisementioning

confidence: 99%

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A comparative study of noise elimination algorithms for a 3D terrain model through object clustering and the differential method

2014

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The technology to automatically detect the surrounding working environment for modeling of the result is the key essential technique for the automation of the construction equipment developments. When noise takes place during 3D modeling of the front work area of the automated construction equipments, the point of noise occurrence during the surface modeling can result in ground level phenomenon in the form of triangular pyramid to lower the quality of the modeling. This can greatly affect the detection of the objects around the automated equipment. This study proposed revised object clustering and differential algorithms for noise elimination of 3D terrain model and compared the noise elimination performance of existing algorithm and proposed algorithm on the images of actual earthwork working environment. It is expected that the noise elimination algorithm proposed in this study will be very useful as a widely used essential technology required for the development of automated technology not only in earthwork field but also in other general construction and civil engineering fields by noise elimination of the 3D working environment model.

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Section: Previous Research On Noise Elimination Algorithmsmentioning

confidence: 99%

Section: Review Of the Problems Of Previous Studies On Noisementioning

confidence: 99%

A comparative study of noise elimination algorithms for a 3D terrain model through object clustering and the differential method

2014

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“…5 will provide a basis for accurately tuning parameter . The analysis of (12)- (15) has been carried out with normally distributed , however the approximations of (14)-(15) will be valid for other distributions, whenever .…”

Section: B Synchronization Efficiencymentioning

confidence: 99%

“…Also shown in Fig. 13, the data are fit to the linearized selection efficiency model given by (15). For this fit, the slope of the curves is given by , which was determined in Section IV-A, and the peak is set to 100% selection efficiency, leaving as the only unknown parameter.…”

Section: B Tuning the Image Selection Parameter Boundary Tuningmentioning

confidence: 99%

“…Sempere and Silvestre describe the limitations of software triggers (an asynchronous message over the network), even in a high-speed network [2]. Many reports describe the use of serial interface cameras in applications requiring synchronization (e.g., [2], [14], [15] Here, a kernel-based mechanism for reliable soft synchronization is introduced. Stochastic elements of the mechanism are modeled and the synchronization uncertainty is considered in detail, leading to an efficient method for calibrating the synchronization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Soft Synchronization: Synchronization for Network-Connected Machine Vision Systems

Armstrong

Veettil²

2007

IEEE Trans. Ind. Inf.

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Many computer vision applications require synchronization between image acquisition and external trigger events. Hardware and software triggering are widely used but have several limitations. Soft synchronization is investigated, which operates by time tagging both trigger events and images in a video stream and selecting the image corresponding to each trigger event. A stochastic model is developed for soft synchronization; and, based on the model, the uncertainty interval and confidence for correct image selection are determined, and an efficient calibration method is derived.Soft synchronization is experimentally demonstrated on a Linux image processing computer with a camera connected by the IEEE-1394 serial bus. To minimize timing variability, time tags are associated with images and events in the corresponding interrupt service routines within the operating system of the image processing computer. The model-based analysis applied to the experimental hardware shows that image/event synchronization within 1 2 inter-frame interval can be achieved with 99% confidence. Experiments confirm this result.

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Research on Key Technology of Welding Robot Positioning System Based on Vision Feedback

Qiao¹,

Dong²,

Tao³

2011

Energy Procedia

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Stereo vision in LHD automation

Cited by 10 publications

References 20 publications

A comparative study of noise elimination algorithms for a 3D terrain model through object clustering and the differential method

A comparative study of noise elimination algorithms for a 3D terrain model through object clustering and the differential method

Soft Synchronization: Synchronization for Network-Connected Machine Vision Systems

Research on Key Technology of Welding Robot Positioning System Based on Vision Feedback

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