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A Cross Structured Light Sensor and Stripe Segmentation Method for Visual Tracking of a Wall Climbing Robot.

Zhang L, Sun J, Yin G, Zhao J, Han Q - Sensors (Basel) (2015)

Bottom Line: An adaptive monochromatic space is applied to preprocess the image with ambient noises.In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution.Lastly, the stripe centre points are extracted from the image.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China. zhangliguo@hrbeu.edu.cn.

ABSTRACT
In non-destructive testing (NDT) of metal welds, weld line tracking is usually performed outdoors, where the structured light sources are always disturbed by various noises, such as sunlight, shadows, and reflections from the weld line surface. In this paper, we design a cross structured light (CSL) to detect the weld line and propose a robust laser stripe segmentation algorithm to overcome the noises in structured light images. An adaptive monochromatic space is applied to preprocess the image with ambient noises. In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution. Lastly, the stripe centre points are extracted from the image. In experiments, the CSL sensor and the proposed algorithm are applied to guide a wall climbing robot inspecting the weld line of a wind power tower. The experimental results show that the CSL sensor can capture the 3D information of the welds with high accuracy, and the proposed algorithm contributes to the weld line inspection and the robot navigation.

No MeSH data available.


Related in: MedlinePlus

The absolute errors in feature points.
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sensors-15-13725-f015: The absolute errors in feature points.

Mentions: Table 5 lists the measurement accuracy evaluation data. There are Root-Mean-Square (RMS) errors of (0.094, 0.034, 0.120) in the three directions. The measurement errors are shown in Figure 14 and Figure 15.


A Cross Structured Light Sensor and Stripe Segmentation Method for Visual Tracking of a Wall Climbing Robot.

Zhang L, Sun J, Yin G, Zhao J, Han Q - Sensors (Basel) (2015)

The absolute errors in feature points.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507594&req=5

sensors-15-13725-f015: The absolute errors in feature points.
Mentions: Table 5 lists the measurement accuracy evaluation data. There are Root-Mean-Square (RMS) errors of (0.094, 0.034, 0.120) in the three directions. The measurement errors are shown in Figure 14 and Figure 15.

Bottom Line: An adaptive monochromatic space is applied to preprocess the image with ambient noises.In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution.Lastly, the stripe centre points are extracted from the image.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China. zhangliguo@hrbeu.edu.cn.

ABSTRACT
In non-destructive testing (NDT) of metal welds, weld line tracking is usually performed outdoors, where the structured light sources are always disturbed by various noises, such as sunlight, shadows, and reflections from the weld line surface. In this paper, we design a cross structured light (CSL) to detect the weld line and propose a robust laser stripe segmentation algorithm to overcome the noises in structured light images. An adaptive monochromatic space is applied to preprocess the image with ambient noises. In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution. Lastly, the stripe centre points are extracted from the image. In experiments, the CSL sensor and the proposed algorithm are applied to guide a wall climbing robot inspecting the weld line of a wind power tower. The experimental results show that the CSL sensor can capture the 3D information of the welds with high accuracy, and the proposed algorithm contributes to the weld line inspection and the robot navigation.

No MeSH data available.


Related in: MedlinePlus