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.

Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging) point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional pylon modeling (MD3DM) method using airborne LiDAR data. We start with constructing a parametric model of pylon, based on its actual structure and the characteristics of point clouds data. In this model, a pylon is divided into three parts: pylon legs, pylon body and pylon head. The modeling approach mainly consists of four steps. Firstly, point clouds of individual pylon are detected and segmented from massive high-voltage transmission corridor point clouds automatically. Secondly, an individual pylon is divided into three relatively simple parts in order to reconstruct different parts with different strategies. Its position and direction are extracted by contour analysis of the pylon body in this stage. Thirdly, the geometric features of the pylon head are extracted, from which the head type is derived with a SVM (Support Vector Machine) classifier. After that, the head is constructed by seeking corresponding model from pre-build model library. Finally, the body is modeled by fitting the point cloud to planes. Experiment results on several point clouds data sets from China Southern high-voltage nationwide transmission grid from Yunnan Province to Guangdong Province show that the proposed approach can achieve the goal of automatic three-dimensional modeling of the pylon effectively.

Track irregularity is one of the most important factors in the regular maintenance and overhaul work needed to determine the state of a railway for safe operation. In this paper, a mobile approach to the measurement of subway track irregularity is proposed. A high-precision three-dimensional (3D) curve of railway track was surveyed by a track trolley equipped with a laser-aided inertial navigation system (INS)/odometer navigation system. Models for measuring the alignment, vertical, cant, and twist irregularities were established. These four types of irregularities were estimated using the 3D track curve. To solve the problem of inherent drift of the INS/odometer system, a laser scanner on the trolley was used to obtain observations of control points along the rail track for position updates. Measurements of real track irregularities were conducted to validate the proposed method. The experimental results indicate that this approach has good repeatability. The accuracy of track-irregularity measurements meets the technical requirements of the Shenzhen Metro in China.

With the rapid development of robotics, miniaturized and inexpensive robots have gradually entered the public's field of vision. Smartphone-based robots have the potential to provide high-precision indoor localization, and they are easy to promote and apply. In this paper, we propose a tight integrated positioning system based on Wi-Fi round trip time (RTT), encoders, and the inertial measurement unit (IMU) for robot indoor localization using smartphones. In our approach, we first design an error-state Kalman Filter (ESKF) to fuse the inertial information from the IMU with the measurements from the encoders to suppress the errors accumulated by the inertial navigation system (INS). Second, the Wi-Fi RTT-based localization method is implemented through an adaptive extended Kalman filter (AEKF) to fuse the ranging information. Finally, to overcome the shortcomings of the long-time drift of the INS and the instability of the Wi-Fi RTT-based localization system, we implement a tight integrated positioning system, which obtains the optimal estimation of the INS positioning error by using the Wi-Fi RTT ranging values as filtering constraints and smooths the INS positioning error through the Rauch-Tung-Striebel (RTS) algorithm. Experimental results show that compared with the Wi-Fi RTT-based method under line of sight condition (LOS) and non-line of sight condition (NLOS), the mean positioning error of the proposed method is improved by 54.62% and 58.38%, respectively, while compared with the INS/Encoder method, those is improved by 57.48% and 33.04%, respectively.

This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.