Learning and SLAM Based Decision Support Platform for Sewer Inspection
Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing sy...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/6/968 |
| _version_ | 1818321531474804736 |
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| author | Tzu-Yi Chuang Cheng-Che Sung |
| author_facet | Tzu-Yi Chuang Cheng-Che Sung |
| author_sort | Tzu-Yi Chuang |
| collection | DOAJ |
| description | Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance. |
| first_indexed | 2024-12-13T10:42:23Z |
| format | Article |
| id | doaj.art-0e5fb0217dd540a089fba3778d0d3abd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:42:23Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0e5fb0217dd540a089fba3778d0d3abd2022-12-21T23:50:26ZengMDPI AGRemote Sensing2072-42922020-03-0112696810.3390/rs12060968rs12060968Learning and SLAM Based Decision Support Platform for Sewer InspectionTzu-Yi Chuang0Cheng-Che Sung1Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanRoutine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.https://www.mdpi.com/2072-4292/12/6/968decision supportdefect recognitionindoor positioningobstacle detectionslam |
| spellingShingle | Tzu-Yi Chuang Cheng-Che Sung Learning and SLAM Based Decision Support Platform for Sewer Inspection Remote Sensing decision support defect recognition indoor positioning obstacle detection slam |
| title | Learning and SLAM Based Decision Support Platform for Sewer Inspection |
| title_full | Learning and SLAM Based Decision Support Platform for Sewer Inspection |
| title_fullStr | Learning and SLAM Based Decision Support Platform for Sewer Inspection |
| title_full_unstemmed | Learning and SLAM Based Decision Support Platform for Sewer Inspection |
| title_short | Learning and SLAM Based Decision Support Platform for Sewer Inspection |
| title_sort | learning and slam based decision support platform for sewer inspection |
| topic | decision support defect recognition indoor positioning obstacle detection slam |
| url | https://www.mdpi.com/2072-4292/12/6/968 |
| work_keys_str_mv | AT tzuyichuang learningandslambaseddecisionsupportplatformforsewerinspection AT chengchesung learningandslambaseddecisionsupportplatformforsewerinspection |