In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order Remote Sens. 2015, 7 7403 to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

Most existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these methods have limitations due to the presence of unpredictable sewer pipeline fault patterns. Deep learning methods have also been applied to sewer pipeline fault detection. However, these methods require a large amount of annotated data to obtain reliable results. In this paper, we propose a fault detection method that applies unsupervised machine learning based anomaly detection algorithms with feature extraction to videos recorded by new sewer pipeline visual inspection equipment. The recorded videos are regarded as sequence signals, which are converted into feature vectors, followed by application of an anomaly detection algorithm. Unlike existing methods, the proposed method is computationally efficient as it does not require an annotated fault sample database for training fault detection models. We evaluate various anomaly detection algorithms and feature combinations on real sewer pipeline data collected in Shenzhen, with an overall accuracy result of above 90%. The proposed method provides a new and fast technique for surveying urban sewer pipelines, and to facilitate further research in this area, we have made the code and data used in this paper publicly available.

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Submarine pipelines are important resource delivery devices between land and ocean. For safety reasons, pipelines are often embedded beneath the seabed at a certain depth, to reduce the risk of direct damage to the pipeline. In the past, various kinds of detection equipment have been used for pipeline inspection, to ensure the normal operation of pipelines in practical applications. Acoustic detection technology is the dominant method to monitor buried submarine pipelines. Extracting and integrating the information in acoustic images, such as the route and burial depth, can help to monitor the status of a pipeline. However, most of the existing methods are based on limited parameters, and they cannot be used to precisely detect and locate a submarine pipeline under complex conditions. In this study, a multi-sensor surveying system was used, which integrates a sub-bottom profiler (SBP) and the Shipborne Over-and Under-Water Integrated Mobile Mapping System (SiOUMMS) on the same ship. The data acquired in this system include acoustic profile images and the over-and under-water topography of the pipeline route area. We also designed a position deviation correction method to improve the accuracy of the pipeline detection positioning, i.e., pipeline positioning correction in the real-time kinematic (RTK) positioning data and pipeline horizontal route correction in the integrated data. Compared with the uncorrected pipeline detection positioning result, the reliability of the pipeline inspection result is greatly improved, and the effectiveness and merit of the proposed method are clearly demonstrated. Finally, we conducted a buried pipeline safety assessment for the installation of newly designed wharf piles at Mawan Port of Shenzhen, China, where the results showed that one of the first rows of wharf piles would collide with the sewage pipeline.INDEX TERMS Over-and under-water topography, submarine pipeline, acoustic profile images, buried pipeline detection.