Graph-Based Robot Localization in Tunnels Using RF Fadings

Seco, Teresa; Lázaro, María Teresa; Rizzo, Carlos; Espelosín, Jesús; Villarroel, José Luis

doi:10.1007/978-3-030-35990-4_47

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…Pose-graph optimization has been applied to localization of robots in pipes using vision [ 7 ], a fusion of IMU and tether cable information [ 17 ], and periodic radio wave amplitude [ 41 ]. Recent work in the use of acoustic echoes for localization in pipes [ 42 ] could be applied to pose-graph optimization; these acoustic measurements are range-only measurements of features, which have been used in pose-graph optimization in other applications [ 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

Worley

Sailor

et al. 2020

Sensors

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One of the most fundamental tasks for robots inspecting water distribution pipes is localization, which allows for autonomous navigation, for faults to be communicated, and for interventions to be instigated. Pose-graph optimization using spatially varying information is used to enable localization within a feature-sparse length of pipe. We present a novel method for improving estimation of a robot’s trajectory using the measured acoustic field, which is applicable to other measurements such as magnetic field sensing. Experimental results show that the use of acoustic information in pose-graph optimization reduces errors by 39% compared to the use of typical pose-graph optimization using landmark features only. High location accuracy is essential to efficiently and effectively target investment to maximise the use of our aging pipe infrastructure.

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Section: Discussionmentioning

confidence: 99%

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

Worley

Sailor

et al. 2020

Sensors

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“…Information about the estimated position of the minimum

together with its corresponding position in the map

is available ( Figure 9 f). The RF minimum detection method is explained in detail in [ 4 ].…”

Section: Discrete Features Detection: Galleries and Fadingsmentioning

confidence: 99%

“…By considering the aforementioned studies and recent advances in the field of graph SLAM, our previous work [ 4 ] addressed the robot localization problem in tunnels as an online pose graph localization problem for which we originally introduced the results of our RF signal minima detection method into a graph optimization framework by taking advantage of the periodic nature of RF signals within tunnels. Although the results were very promising, the distance between fadings is usually large (i.e., hundreds of meters), which causes the error to grow too much between detections.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the latter, we propose the complementary use of structural characteristics originating from the safety regulations to increase localization accuracy using only fadings. Thus, the alternative localization approach proposed in this paper builds on our aforementioned work [ 4 ], which is further extended with important new contributions that can be summarized as follows: First, two methods to identify tunnel galleries from onboard LiDAR information as relevant places for a global robot localization have been implemented and validated with real data. A strategy to introduce the results provided by a gallery detector into a pose graph has been developed.…”

Section: Introductionmentioning

confidence: 99%

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Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework

Seco

Lázaro

Espelosín

et al. 2022

Sensors

Self Cite

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Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.

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“…Localization in pipes is challenging due to the unavailability of GPS, the lack of a reliable magnetic field for a magnetometer, the limited perspective of sensors, and the sparseness of recognisable features. Despite this, localization has been demonstrated using a range of sensors, including vision [2], acoustics [3], [4], [5], [6], and radio waves [7]. However, in practice, front-end sensor information is expected to be uncertain and unreliable.…”

Section: Introductionmentioning

confidence: 99%

Robust Efficient Localization of Robots in Pipe Networks using a Particle Filter for Hybrid Metric-Topological Space

Worley

Anderson

2021

2021 European Conference on Mobile Robots (ECMR)

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Water distribution and drainage pipe inspection and maintenance is costly, and could be improved by using robots to locate faults from within the pipes. Robot localization is critical in this operation, but is challenging due to the constraints of the pipe environment. An efficient, robust algorithm is needed for localization using limited sensors. A novel particle filter algorithm is proposed for localization, which estimates the robot's position in a hybrid metric-topological state space, allowing efficient computation and relocalization. The algorithm is demonstrated in simulation at a large scale, considering substantial uncertainty in motion, measurements, and the map of the environment, showing an improvement over a benchmark algorithm developed for this application.

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Graph-Based Robot Localization in Tunnels Using RF Fadings

Cited by 6 publications

References 9 publications

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework

Robust Efficient Localization of Robots in Pipe Networks using a Particle Filter for Hybrid Metric-Topological Space

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