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Article

Learning and SLAM Based Decision Support Platform for Sewer Inspection

Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
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Remote Sens. 2020, 12(6), 968; https://doi.org/10.3390/rs12060968
Received: 31 January 2020 / Revised: 13 March 2020 / Accepted: 16 March 2020 / Published: 17 March 2020
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. View Full-Text
Keywords: decision support; defect recognition; indoor positioning; obstacle detection; SLAM decision support; defect recognition; indoor positioning; obstacle detection; SLAM
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MDPI and ACS Style

Chuang, T.-Y.; Sung, C.-C. Learning and SLAM Based Decision Support Platform for Sewer Inspection. Remote Sens. 2020, 12, 968. https://doi.org/10.3390/rs12060968

AMA Style

Chuang T-Y, Sung C-C. Learning and SLAM Based Decision Support Platform for Sewer Inspection. Remote Sensing. 2020; 12(6):968. https://doi.org/10.3390/rs12060968

Chicago/Turabian Style

Chuang, Tzu-Yi, and Cheng-Che Sung. 2020. "Learning and SLAM Based Decision Support Platform for Sewer Inspection" Remote Sensing 12, no. 6: 968. https://doi.org/10.3390/rs12060968

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