3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
Automating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intens...
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2021-04-01
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Online Access: | https://www.mdpi.com/1424-8220/21/7/2553 |
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doaj-1b855a1a4c9d456e94e85bac70f18ff12021-04-06T23:02:10ZengMDPI AGSensors1424-82202021-04-01212553255310.3390/s210725533D Sensors for Sewer Inspection: A Quantitative Review and AnalysisChris H. Bahnsen0Anders S. Johansen1Mark P. Philipsen2Jesper W. Henriksen3Kamal Nasrollahi4Thomas B. Moeslund5Visual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkAutomating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intensive, and expensive and presents a huge potential for automation. With this work, we address a central component of the next generation of robotic inspection of sewers, namely the choice of 3D sensing technology. We investigate three prominent techniques for 3D vision: passive stereo, active stereo, and time-of-flight (ToF). The Realsense D435 camera is chosen as the representative of the first two techniques wheres the PMD CamBoard pico flexx represents ToF. The 3D reconstruction performance of the sensors is assessed in both a laboratory setup and in an outdoor above-ground setup. The acquired point clouds from the sensors are compared with reference 3D models using the cloud-to-mesh metric. The reconstruction performance of the sensors is tested with respect to different illuminance levels and different levels of water in the pipes. The results of the tests show that the ToF-based point cloud from the pico flexx is superior to the output of the active and passive stereo cameras.https://www.mdpi.com/1424-8220/21/7/2553sewer inspectionsewer pipes3D vision3D reconstructioncomputer visionautomated inspection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chris H. Bahnsen Anders S. Johansen Mark P. Philipsen Jesper W. Henriksen Kamal Nasrollahi Thomas B. Moeslund |
spellingShingle |
Chris H. Bahnsen Anders S. Johansen Mark P. Philipsen Jesper W. Henriksen Kamal Nasrollahi Thomas B. Moeslund 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis Sensors sewer inspection sewer pipes 3D vision 3D reconstruction computer vision automated inspection |
author_facet |
Chris H. Bahnsen Anders S. Johansen Mark P. Philipsen Jesper W. Henriksen Kamal Nasrollahi Thomas B. Moeslund |
author_sort |
Chris H. Bahnsen |
title |
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis |
title_short |
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis |
title_full |
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis |
title_fullStr |
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis |
title_full_unstemmed |
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis |
title_sort |
3d sensors for sewer inspection: a quantitative review and analysis |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-04-01 |
description |
Automating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intensive, and expensive and presents a huge potential for automation. With this work, we address a central component of the next generation of robotic inspection of sewers, namely the choice of 3D sensing technology. We investigate three prominent techniques for 3D vision: passive stereo, active stereo, and time-of-flight (ToF). The Realsense D435 camera is chosen as the representative of the first two techniques wheres the PMD CamBoard pico flexx represents ToF. The 3D reconstruction performance of the sensors is assessed in both a laboratory setup and in an outdoor above-ground setup. The acquired point clouds from the sensors are compared with reference 3D models using the cloud-to-mesh metric. The reconstruction performance of the sensors is tested with respect to different illuminance levels and different levels of water in the pipes. The results of the tests show that the ToF-based point cloud from the pico flexx is superior to the output of the active and passive stereo cameras. |
topic |
sewer inspection sewer pipes 3D vision 3D reconstruction computer vision automated inspection |
url |
https://www.mdpi.com/1424-8220/21/7/2553 |
work_keys_str_mv |
AT chrishbahnsen 3dsensorsforsewerinspectionaquantitativereviewandanalysis AT anderssjohansen 3dsensorsforsewerinspectionaquantitativereviewandanalysis AT markpphilipsen 3dsensorsforsewerinspectionaquantitativereviewandanalysis AT jesperwhenriksen 3dsensorsforsewerinspectionaquantitativereviewandanalysis AT kamalnasrollahi 3dsensorsforsewerinspectionaquantitativereviewandanalysis AT thomasbmoeslund 3dsensorsforsewerinspectionaquantitativereviewandanalysis |
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