A Robust Localization System for Inspection Robots in Sewer Networks †

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2

Position and orientation correction for pipe profiling robots

Sewer pipelines are prevalent, important, valuable, unnoticed, and often in a state of disrepair. Pipeline inspection is essential for effective management of wastewater systems and is now mandated for many municipalities complying with the Governmental Accounting Standards Board Statement 34 and EPA regulations. Pipe inspection robots are routinely used to inspect underground pipelines for cracks, deformations, leaks, blockages and other anomalies to prevent catastrophic failure and to ensure cost effective maintenance and renewal. Most existing pipe inspection robots only collect video footage of pipe condition. Pipe profiling technology has recently been introduced to allow for measurement of the internal coordinate geometry of pipelines. Accurate radial measurements permit the calculation of several important pipe parameters which aid in the determination of pipe condition and prediction of time to failure. Significant research work has been completed in North America, Europe, Asia and Australia aimed at improving the accuracy and automation of the pipe inspection process. However, standard calibration, verification, reporting and analysis practices must be developed for pipe profilers if coordinate profiling data is to be effectively included in the long term management of pipeline assets. The objective of this research is to quantify the measurement error incurred by a pipe profiler\u27s misalignment with the pipe axis, present a new methodology to correct the measurement error, develop a prototype profiler to verify the equations derived herein, and to further the development of pipe profiler technology at the Trenchless Technology Center at Louisiana Tech University. Equations are derived for pipe ovality as a function of the robot\u27s position and orientation with respect to a pipe to demonstrate the magnitude of the error which is introduced by a robot\u27s misalignment with the pipe axis. A new technique is presented to estimate the position and orientation of a profiler using radial measurement devices at each of its ends. This technique is demonstrated by applying homogeneous coordinate transformations to simulated radial measurements based on mathematically generated data that would be obtained by incrementally rotating two parallel radial measuring devices in a perfectly cylindrical pipe. A prototype pipe profiling robot was developed to demonstrate the new position and orientation technique and to experimentally verify the measurement error caused by a robot\u27s misalignment with the pipe axis. This work improves the accuracy and automation of pipe profiling technology and makes a case for the development of industry standard calibration, verification, reporting and analysis practices

Autonomous pipeline monitoring and maintenance system: a RFID-based approach

Pipeline networks are one of the key infrastructures of our modern life. Proactive monitoring and frequent inspection of pipeline networks are very important for sustaining their safe and efficient functionalities. Existing monitoring and maintenance approaches are costly and inefficient because pipelines can be installed in large scale and in an inaccessible and hazardous environment. To overcome these challenges, we propose a novel Radio Frequency IDentification (RFID)-based Autonomous Maintenance system for Pipelines, called RAMP, which combines robotic, sensing, and RFID technologies for efficient and accurate inspection, corrective reparation, and precise geo-location information. RAMP can provide not only economical and scalable remedy but also safe and customizable solution. RAMP also allows proactive and corrective monitoring and maintenance of pipelines. One prominent advantage of RAMP is that it can be applied to a large variety of pipeline systems including water, sewer, and gas pipelines. Simulation results demonstrate the feasibility and superior performance of RAMP in comparison to the existing pipeline monitoring systems

Measuring the interior of in-use sewage pipes using 3D vision

Sewage pipes may be renovated using tailored linings. However, the interior diameter of the pipes must be measured prior to renovation. This paper investigates the use of 3D vision sensors for measuring the interior diameter of sewage pipes, removing the need for human entry in the pipes. The 3D sensors are residing in a waterproof box that is lowered into the well. A RANSAC-based method is used for cylinder estimation from the acquired point clouds of the pipe and the diameter of these cylinders is used as a measure of the interior pipe diameter. The method is tested in 74 real-world sewage pipes with diameters between 150- and 1100 mm. The diameter of 68 pipes is measured within a tolerance of ±20mm whereas 8 pipes are above. It was found that the faulty estimates can be detected in the field using a combination of human-in-the-loop qualitative and quantitative data-driven measures.</p

Single-pass inline pipeline 3D reconstruction using depth camera array

A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. In the reconstructed model of a longer pipeline, the proposed method generates the dense 3D surface reconstruction model at the millimeter level accuracy with less than 0.5% distance error. The achieved performance highlights its potential as a useful tool for efficient in-line, non-destructive evaluation of pipeline assets

A robust method for approximate visual robot localization in feature-sparse sewer pipes

Buried sewer pipe networks present many challenges for robot localization systems, which require non-standard solutions due to the unique nature of these environments: they cannot receive signals from global positioning systems (GPS) and can also lack visual features necessary for standard visual odometry algorithms. In this paper, we exploit the fact that pipe joints are equally spaced and develop a robot localization method based on pipe joint detection that operates in one degree-of-freedom along the pipe length. Pipe joints are detected in visual images from an on-board forward facing (electro-optical) camera using a bag-of-keypoints visual categorization algorithm, which is trained offline by unsupervised learning from images of sewer pipe joints. We augment the pipe joint detection algorithm with drift correction using vision-based manhole recognition. We evaluated the approach using real-world data recorded from three sewer pipes (of lengths 30, 50 and 90 m) and benchmarked against a standard method for visual odometry (ORB-SLAM3), which demonstrated that our proposed method operates more robustly and accurately in these feature-sparse pipes: ORB-SLAM3 completely failed on one tested pipe due to a lack of visual features and gave a mean absolute error in localization of approximately 12%–20% on the other pipes (and regularly lost track of features, having to re-initialize multiple times), whilst our method worked successfully on all tested pipes and gave a mean absolute error in localization of approximately 2%–4%. In summary, our results highlight an important trade-off between modern visual odometry algorithms that have potentially high precision and estimate full six degree-of-freedom pose but are potentially fragile in feature sparse pipes, versus simpler, approximate localization methods that operate in one degree-of-freedom along the pipe length that are more robust and can lead to substantial improvements in accuracy