A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization

Gunatilake, Amal; Kodagoda, Sarath; Thiyagarajan, Karthick

doi:10.1109/lra.2022.3163769

Cited by 19 publications

(11 citation statements)

References 36 publications

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“…Localization approach using IMU, gyro, and the leak sensors proposed in [28] 250 Not Given Encoder based method 833 214 Proposed GP based PF method in [34] 1.8 A mobile robotic prototype which is capable of navigating in pipes with diameters ranging from 450mm to 650mm was developed. A signal cross-correlation mapping technique in combination with a customized simultaneous localization and mapping algorithm was developed to estimate the location of the robot.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The Gaussian process combined particle filter localization methodology using UHF-RFID signals has been proposed in our previous research [31], [32], [34], and it takes advantage of both Received Signal Strength Indicator (RSSI) and Phase data values to improve accuracy. It works well with known RFID distribution maps (RFID tag locations are known), and it has millimetre-level accuracy when using known RFID distribution maps.…”

Section: Introductionmentioning

confidence: 99%

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Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception

2022

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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 posegraph 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.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception

2022

Self Cite

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“…Sensing methods specialised for buried pipes have also been deployed on robots. Radio waves have been used to produce a recognisable signal which varies spatially along the length of a pipe [14], and radio signals has been used in pipes with RFID tags [15]. The time of travel of an acoustic signal has been used on a tethered robot to estimate the distance from a separate transmitter [16].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Echo Sensing for Robot Localization in Buried Pipe Networks

Worley¹,

Yu²,

Horoshenkov³

et al. 2023

Preprint

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<p>The abstract of this pre-print paper is: Robot localization in buried pipes presents a number of challenges, including the unavailability of methods such as a global positioning system, and the limited perspective of sensors such as vision. This paper addresses these challenges by using acoustic sensing, where signals sent by robots propagate long distances and around corners in the pipe environment, and can be used to estimate the distance to reflective features. It is shown that the reverberant environment causes a number of echoes which contain useful information. A novel algorithm for using this information in pose-graph optimization is proposed, and is shown to be necessary in comparison to a naive approach. The algorithm is demonstrated to be accurate and robust by measuring the error rate, which is the proportion of estimate error that was greater than a target threshold of 0.5 metres. The median error rate was 0 for measurement uncertainty 2.5 times larger than that found experimentally, and for motion uncertainty 4 times that which could be tolerated by an idealised loop-closure approach used for comparison. This work is the foundation for new data fusion methods for robot localization in buried pipes.</p>

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“…The visual SLAM methods of robot-carried cameras, such as ORB-SLAM3 [3], PL-SLAM [4], SVO2 [5], are easy to lose features in the process of motion tracking. Wireless positioning methods such as WiFi [6], radio frequency identification (RFID) [7][8] and ultra wide band (UWB) [9] methods are widely used in indoor, tunnel, factory et al, but the multi-path reflection caused by the absorption and occlusion of steel components to signals affects the positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

Localization of Wall Climbing Robot on Cylinder-Shaped Steel

Zhang

Huang

Sun

2022

Advances in Transdisciplinary Engineering

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Due to the limitation of sensor application in special environment such as relatively closed and magnetic interference environment, the positioning and the heading angle errors of wall climbing robots accumulate with time. This paper proposes a difference projection localization method based on an external RGB-D camera and a robot-carried inertial measurement unit (IMU). We differential the depth image to obtain the distance change due to the robot occupancy. Then, the 3D point cloud information is converted into 2D image information by projecting the above distances along the normal vector of the robot chassis, which greatly speeds up the computational speed. The position of the robot is calculated by studying the statistical characteristics of the projection. Two EKFs are designed to estimate the attitude, taking the gravity vector and the normal vector of the robot chassis as observation. The experimental results show that the localization error of the wall climbing robot is within 0.017m, and the heading angle error of the attitude estimation is within 3.1∘. The obtained results prove its applicability in self-localization of the wall climbing robot.

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A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization

Cited by 19 publications

References 36 publications

Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception

Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception

Acoustic Echo Sensing for Robot Localization in Buried Pipe Networks

Localization of Wall Climbing Robot on Cylinder-Shaped Steel

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