Water utilities across the globe are concerned with the inspection and replacement of buried metallic water pipes due to corrosion-related structural damages. Internal pipe linings are commonly used as a renewal method to improve structural strength as they are regarded to be a less expensive alternative to costly and time-consuming pipe replacements. However, linings are also prone to failure as well. Therefore, water authorities regularly monitor lining performance, where defect evolution over a long period of time is an important parameter to note. It requires an accurate in-pipe robot localization technology. In this article, we propose a novel method for in-pipe robot localization and tag mapping that uses battery-free UHF-RFID sensor wireless signals. It utilizes a signal mapping approach in combination with a tailored pose-graph simultaneous localization and mapping algorithm. Evaluation results of a field-extracted pipe sample from Sydney Water’s distribution network show the proposed approach is capable of localizing the robot within 2.5cm accuracy in a 50m equivalent pipe with an unknown UHF-RFID distribution. The proposed approach outperformed other reported similar work in the literature.

A reliable robotic localization method is required for comparing three-dimensional pipe maps obtained via laser scans at various times for accurately monitoring the evolution of internal pipe surface defects. Existing robotic localization methods have limitations when visual features vanish due to changes in the pipe environment or when encoder data becomes highly uncertain due to long-distance robotic traverses. To address this issue, we leverage battery-free ultra-high frequency radio frequency identification (UHF-RFID) sensors for transmitting wireless signals to a two-antenna reader integrated mobile robotic system. Although there are literature on the investigation of UHFRFID behaviors and their applications in indoor environments, analysis of the same for in-pipe scenarios was not well studied. In this paper, we evaluate the UHF-RFID sensor signals inside a field extracted pipeline. Firstly, we examine the UHF-RFID sensor signal patterns through repeated robotic scans. Secondly, we examine how the placement of UHF-RFID reader antennas affects the transmission of UHF-RFID sensor signals, as well as we study the effects of robotic traverse direction and speed on the UHF-RFID wireless signals. Finally, we examine whether identical UHF-RFID sensors generate the same pattern when placed in a pipeline. Overall, the experimental evaluation demonstrates that the use of two-antenna UHF-RFID readers can ameliorate the capabilities of robotic localization in the pipeline.

Underground water pipes are important to any country's infrastructure. Overtime, the metallic pipes are prone to corrosion, which can lead to water leakage and pipe bursts. In order to prolong the service life of those assets, water utilities in Australia apply protective pipe linings. Long-term monitoring and timely intervention are crucial for maintaining those lining assets. However, the water utilities do not possess the comprehensive technology to achieve it. The main reasons for lacking such technology are the unavailability of sensors and accurate robot localization technologies. Feature based localization methods such as SLAM has limited use as the application of liners alters the features and the environment. Encoder based localization is not accurate enough to observe the evolution of defects over a long period of time requiring unique defect correspondence. This motivates us to explore accurate contact-less and wireless based localization methods. We propose a cost-effective localization method using UHFRFID signals for robot localization inside pipelines based on Gaussian process combined particle filter. Experiments carried out in field extracted pipe samples from the Sydney water pipe network show that using the RSSI and Phase data together in the measurement model with particle filter algorithm improves the localization accuracy up to 15 centimeters precision.