Remote Absolute Roll-Angle Measurement in Range of 180° Based on Polarization Modulation

Chen, Xiuguo; Liao, Jinbao; Gu, Honggang; Zhang, Chuanwei; Jiang, Hao; Liu, Shiyuan

doi:10.1007/s41871-020-00069-0

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…Angle measurements (roll, pitch, and yaw angles) are important parameters in many different fields, e.g., remote sensing [1], navigation [2] and object tracking [3]. Roll angle is deemed the most challenging measurement quantity because the angular displacement of the roll angle is perpendicular to the probe beam.…”

Section: Background and Motivationmentioning

confidence: 99%

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P62 - An Optical Stokes Absolute Roll-angle Sensor with a Full Measurement Range of 360°

Chen¹,

Beyerer²,

Hartrumpf³

et al. 2023

Poster

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Angle measurements (roll, pitch, and yaw) are important parameters in many different fields. Roll angle is deemed the most challenging measurement quantity because the angular displacement of the roll angle is perpendicular to the probe beam. In this work, we proposed a Stokes roll-angle sensor whose measurement range can achieve 360° without ambiguity. The principle is based on the polarization change of a sensing unit (vortex retarder). Within the whole measuring range, the measurement errors are less than ±0.15°and the standard deviations are less than 0.09°.

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Section: Background and Motivationmentioning

confidence: 99%

“…This drawback decreases the feasibility of long-range measurements. The polarizationbased measurements for the roll angle can be used for remote sensing but the range is limited to 180° [1,5,6].…”

Section: Background and Motivationmentioning

confidence: 99%

P62 - An Optical Stokes Absolute Roll-angle Sensor with a Full Measurement Range of 360°

Chen¹,

Beyerer²,

Hartrumpf³

et al. 2023

Poster

View full text Add to dashboard Cite

show abstract

“…Mathematical description of periodic errors in PI 2 In practice, for the precise determination of rotation angles using PI 2 , the periodic errors should be considered, predicted, and compensated from the measurement results as aforementioned. The periodic errors of PI 2 are originated from the unexpected light by ghost reflections, retardations of uncalibrated BS [1] and/or the extinction ratios of a target polarizer and four polarizers in the PCMOS. The polarized irradiances (I m,0 , I m,45 , I m,90 and I m,135 ) are practically obtained by the PCMOS can be expressed as…”

Section: Polarized Imaging Interpreter For Simultaneous Clocking Metr...mentioning

confidence: 99%

Supplementary document for Polarized imaging interpreter for simultaneous clocking metrology of multiple objects - 5382673.pdf

Joo,

Bin Jeong,

Park

et al. 2021

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Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning

Pandey,

Sahani

2024

Engineering Reports

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This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min).

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Remote Absolute Roll-Angle Measurement in Range of 180° Based on Polarization Modulation

Cited by 10 publications

References 19 publications

P62 - An Optical Stokes Absolute Roll-angle Sensor with a Full Measurement Range of 360°

P62 - An Optical Stokes Absolute Roll-angle Sensor with a Full Measurement Range of 360°

Supplementary document for Polarized imaging interpreter for simultaneous clocking metrology of multiple objects - 5382673.pdf

Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning

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