A Robust Localization System for Inspection Robots in Sewer Networks
| Autor: | Fernando Caballero, Luis Merino, D. Alejo |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Underground robotics
0209 industrial biotechnology Computer science Monte Carlo Localization Real-time computing 02 engineering and technology Simultaneous localization and mapping Network topology Biochemistry Article localization sewer network Analytical Chemistry Sewer network 020901 industrial engineering & automation Odometry Global pose estimation Field robotics 0202 electrical engineering electronic engineering information engineering GPS-denied Sanitary sewer Electrical and Electronic Engineering Instrumentation Pose Monte Carlo localization underground robotics Atomic and Molecular Physics and Optics global pose estimation Localization Robot 020201 artificial intelligence & image processing field robotics Wireless sensor network |
| Zdroj: | Sensors Volume 19 Issue 22 Sensors (Basel, Switzerland) idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s19224946 |
| Popis: | 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. Unión Europea ECHORD ++ 601116 Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C22 |
| Databáze: | OpenAIRE |
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