Tracking Sensor Location by Video Analysis in Double-Shell Tank Inspections
Source abstract
Abstract
Double-shell tanks (DSTs) are a critical part of the infrastructure for nuclear waste management at the U.S. Department of Energy’s Hanford site. They are expected to be used for the interim storage of partially liquid nuclear waste until 2050, which is the target date for completing the immobilization process for all Hanford nuclear waste. At that time, DSTs will have been used about 15 years beyond their original projected lifetime. Consequently, for the next approximately 30 years, Hanford DSTs will undergo periodic nondestructive evaluation (NDE) to ensure their integrity. One approach to perform NDE is to use ultrasonic data from a robot moving through air slots, originally designed for cooling, in the confined space between primary and secondary tanks. Interpreting ultrasonic sensor output requires knowing where measurements were taken with a precision of approximately one inch. Analyzing video acquired during inspection is one approach to tracking sensor location. The top edge of an air slot is easily detected due to the difference in color and texture between the primary tank bottom and the air slot walls. A line fit to this edge is used in a model to calculate the apparent width of the air slot in pixels at targets near the top edge that can be recognized in video images. The apparent width of the air slot at the chosen target in a later video frame determines how far the robot has moved between those frames. Algorithms have been developed that automate target selection and matching in later frames. Tests in a laboratory mockup demonstrated that the method tracks the location of the ultrasonic sensor with the required precision.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Soft Robotics and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering and Applied Sciences, Washington State University Tri-Cities, Richland 99354, WA, USA
Funding
Pacific Northwest National Laboratory
References (17)
Show source-provided references
1. Girardot, C., Venetz, T., Boomer, K., and Johnson, J. (2016, January 6–10). Hanford Single-Shell Tank and Double-Shell Tank Integrity Programs. Proceedings of the Waste Management Symposium, Phoenix, AZ, USA.
2. Denslow, K., Glass, S., Larche, M., Boomer, K., and Gunter, J. (2018, January 18–22). Hanford Under-tank Inspection with Ultrasonic Volumetric Non-destructive Examination Technology. Proceedings of the Waste Management Symposium, Phoenix, AZ, USA.
3. Acoustic guided wave techniques for detecting corrosion damage of electric grounding rods10.1016/j.measurement.2019.106858 ↗
4. Borigo, C., Love, F., Owens, S., and Rose, J.L. (November, January 30). Guided Wave Phased Array Technology for Rapid Inspection of Hanford Double Shell Tank Liners. Proceedings of the ASNT Annual Conference, Nashville, TN, USA.
5. Denslow, K., Moran, T., Larche, M., Glass, S., Boomer, K., Kelly, S., Wooley, T., Gunter, J., Rice, J., and Stewart, D. (2019, January 3–7). Progress on Advancing Robotic Ultrasonic Volumetric Inspection Technology for Hanford Under-tank Inspection. Proceedings of the WM2019 Conference, Phenix, AZ, USA.
6. Hede, A., Wooley, T., Boomer, K., Gunter, J., Soon, G., Nelson, E., and Denslow, K. (2022, January 6–10). Hanford Double-Shell Primary Tank Bottom Inspection Technology Development. Proceedings of the WM2022 Conference, Phenix, AZ, USA.
7. Cree, C., Cater, E., Wang, H., Mo, C., and Miller, J. (2020). Tracking Robot Location in Non-Destructive Evaluation of Double-Shell Tanks. Appl. Sci., 10.10.3390/app10207318 ↗
8. Dobell, C., and Hamilton, G. (November, January 30). Marsupial Miniature Robotic Crawler Development and Deployment in Nuclear Waste Double Shell Tank Refractory Air Slots. Proceedings of the ASNT Annual Conference, Nashville, TN, USA.
9. Guaranteed SLAM—An interval approach10.1016/j.robot.2017.11.009 ↗
10. Embedding SLAM algorithms: Has it come of age?10.1016/j.robot.2017.10.019 ↗
11. Rosebrock, A. (2015, January 19). Triangle Similarity for Object/Marker to Camera Distance. Available online: https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/.
12. Gunter, J.R. Primary Tank Bottom Visual Inspection System Development and Initial Deployment at Tank AP-107. RPP-RPT-61208, November 2018.
13. A Computational Approach to Edge Detection. Technical Report 610.1109/TPAMI.1986.4767851 ↗
14. Szeliski, R. (2011). Computer Vision: Algorithms and Applications, Springer.10.1007/978-1-84882-935-0 ↗
15. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
16. Arandjelovic, R. (2012, January 16–21). Three things everyone should know to improve object retrieval. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR ‘12, Washington, DC, USA. Available online: https://docs.opencv.org/3.4/d5/d6f/tutorial_feature_flann_matcher.html.10.1109/CVPR.2012.6248018 ↗
17. The OpenCV Library