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Open AccessArticle

A Robust Localization System for Inspection Robots in Sewer Networks

1
School of Engineering, Universidad Pablo de Olavide, 41012 Sevilla, Spain
2
Department of Systems Engineering and Automation, Universidad de Sevilla, 41009 Sevilla, Spain
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Alejo, D.; Caballero, F.; Merino, L. RGBD-based Robot Localization in Sewer Networks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017; pp. 4070–4076.
Sensors 2019, 19(22), 4946; https://doi.org/10.3390/s19224946
Received: 8 October 2019 / Revised: 8 November 2019 / Accepted: 10 November 2019 / Published: 13 November 2019
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
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. View Full-Text
Keywords: localization; sewer network; field robotics; Monte Carlo Localization; GPS-denied; underground robotics; global pose estimation localization; sewer network; field robotics; Monte Carlo Localization; GPS-denied; underground robotics; global pose estimation
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Alejo, D.; Caballero, F.; Merino, L. A Robust Localization System for Inspection Robots in Sewer Networks. Sensors 2019, 19, 4946.

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