Robot Localization in a Pipe Network using a Particle Filter with Error Detection and Recovery in a Hybrid Metric-Topological Space

2021 ◽  
Author(s):  
Rob Worley ◽  
Sean Anderson
Keyword(s):  
Topological Space ◽  
Particle Filter ◽  
Error Detection ◽  
Robot Localization ◽  
Pipe Network

scholarly journals Using UHF-RFID Signals for Robot Localization Inside Pipelines

2021 ◽  
Author(s):  
Amal Gunatilake ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Lasitha Piyathilaka ◽  
Poojaben Darji
Keyword(s):  
Particle Filter ◽  
Cost Effective ◽  
Measurement Model ◽  
Underground Water ◽  
Water Utilities ◽  
Robot Localization ◽  
Uhf Rfid ◽  
Localization Accuracy ◽  
Feature Based ◽  
Particle Filter Algorithm

<div>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.</div>

scholarly journals Mobile Robot Localization Based on Gradient Propagation Particle Filter Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 188475-188487
Author(s):  
Heng Zhang ◽  
Jiemao Wen ◽  
Yanli Liu ◽  
Wenqing Luo ◽  
Naixue Xiong
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Robot Localization ◽  
Mobile Robot Localization

scholarly journals Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Jinxiu Zhu ◽  
Xuewu Zhang ◽  
Dongmin Choi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Mobile Network ◽  
High Accuracy ◽  
Robot Localization ◽  
Network Localization ◽  
Mobile Robot Localization

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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