A Robust Localization System for Inspection Robots in Sewer Networks †

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 ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2

Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: a review

At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environmen

Inertial learning and haptics for legged robot state estimation in visually challenging environments

Legged robots have enormous potential to automate dangerous or dirty jobs because they are capable of traversing a wide range of difficult terrains such as up stairs or through mud. However, a significant challenge preventing widespread deployment of legged robots is a lack of robust state estimation, particularly in visually challenging conditions such as darkness or smoke. In this thesis, I address these challenges by exploiting proprioceptive sensing from inertial, kinematic and haptic sensors to provide more accurate state estimation when visual sensors fail. Four different methods are presented, including the use of haptic localisation, terrain semantic localisation, learned inertial odometry, and deep learning to infer the evolution of IMU biases. The first approach exploits haptics as a source of proprioceptive localisation by comparing geometric information to a prior map. The second method expands on this concept by fusing both semantic and geometric information, allowing for accurate localisation on diverse terrain. Next, I combine new techniques in inertial learning with classical IMU integration and legged robot kinematics to provide more robust state estimation. This is further developed to use only IMU data, for an application entirely different from robotics: 3D reconstruction of bone with a handheld ultrasound scanner. Finally, I present the novel idea of using deep learning to infer the evolution of IMU biases, improving state estimation in exteroceptive systems where vision fails. Legged robots have the potential to benefit society by automating dangerous, dull, or dirty jobs and by assisting first responders in emergency situations. However, there remain many unsolved challenges to the real-world deployment of legged robots, including accurate state estimation in vision-denied environments. The work presented in this thesis takes a step towards solving these challenges and enabling the deployment of legged robots in a variety of applications

Application of a mobile robot to spatial mapping of radioactive substances in indoor environment

Nuclear medicine requires the use of radioactive substances that can contaminate critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. The present work uses a mobile robot in real environment and 3D simulation to develop a method to realize spatial mapping of radioactive substances. The robot should visit all the waypoints arranged in a grid of connectivity that represents the environment. The work presents the methodology to perform the path planning, control and estimation of the robot location. For path planning two methods are approached, one a heuristic method based on observation of problem and another one was carried out an adaptation in the operations of the genetic algorithm. The control of the actuators was based on two methodologies, being the first to follow points and the second to follow trajectories. To locate the real mobile robot, the extended Kalman filter was used to fuse an ultra-wide band sensor with odometry, thus estimating the position and orientation of the mobile agent. The validation of the obtained results occurred using a low cost system with a laser range finder.A medicina nuclear requer o uso de substâncias radioativas que pode vir a contaminar áreas críticas, onde a presença de um ser humano deve ser reduzida ou evitada. O presente trabalho utiliza um robô móvel em ambiente real e em simulação 3D para desenvolver um método para o mapeamento espacial de substâncias radioativas. O robô deve visitar todos os waypoinst dispostos em uma grelha de conectividade que representa o ambiente. O trabalho apresenta a metodologia para realizar o planejamento de rota, controle e estimação da localização do robô. Para o planejamento de rota são abordados dois métodos, um baseado na heurística ao observar o problema e ou outro foi realizado uma adaptação nas operações do algoritmo genético. O controle dos atuadores foi baseado em duas metodologias, sendo a primeira para seguir de pontos e a segunda seguir trajetórias. Para localizar o robô móvel real foi utilizado o filtro de Kalman extendido para a fusão entre um sensor ultra-wide band e odometria, estimando assim a posição e orientação do agente móvel. A validação dos resultados obtidos ocorreu utilizando um sistema de baixo custo com um laser range finder

Robot Localization in Tunnel-like Environments.

Los entornos confinados como tuberías, túneles o minas constituyen infraestructuras clave para el desarrollo de las economías de los diferentes países. La existencia de estas infraestructuras conlleva la necesidad de llevar a cabo una serie de tareas de mantenimiento mediante inspecciones regulares para asegurar la integridad estructural de las mismas. Así mismo, existen otras tareas que se tienen que realizar en estos entornos como pueden ser misiones de rescate en caso de accidentes e incluso las propias tareas derivadas de la construcción de los mismos. La duras condiciones de este tipo de entornos, ausencia de luz, polvo, presencia de fluidos e incluso de sustancias tóxicas, hace que la ejecución de las mismas suponga un trabajo tedioso e incluso peligroso para las personas. Todo esto, unido a los continuos avances en las tecnologías robóticas, hacen que los robots sean los dispositivos más adecuados para la realización de estas tareas.Para que un robot pueda desempeñar su cometido de manera autónoma, es fundamental que pueda localizarse de manera precisa, no sólo para poder decidir las acciones a llevar a cabo sino también para poder ubicar de manera inequívoca los posibles daños que se puedan detectar durante las labores de inspección. El problema de la localización ha sido ampliamente estudiado en el mundo de la robótica, existiendo multitud de soluciones tanto para interiores como para exteriores mediante el uso de diferentes sensores y tecnologías. Sin embargo, los entornos tipo túnel presentan una serie de características específicas que hacen que la tarea de localización se convierta en todo un reto. La ausencia de iluminación y de características distinguibles tanto visuales como estructurales, hacen que los métodos tradicionales de localización basados en sensores láser y cámaras no funcionen correctamente. Además, al tratarse de entornos confinados, no es posible utilizar sensores típicos de exteriores como es el caso del GPS. La presencia de fluidos e incluso de superficies irregulares hacen poco fiables los métodos basados en odometría utilizando encoders en las ruedas del robot.Por otra parte, estos entornos presentan un comportamiento peculiar en lo que a la propagación de la señal de radiofrecuencia se refiere. Por un lado, a determinadas frecuencias, se comportan como guías de onda extendiendo el alcance de la comunicación, pero por otro, la señal radio sufre fuertes desvanecimientos o fadings. Trabajos previos han demostrado que es posible obtener fadings periódicos bajo una configuración determinada.Partiendo de estos estudios, en esta tesis se aborda el problema de la localización en tuberías y túneles reaprovechando esta naturaleza periódica de la señal radio. Inicialmente, se propone un método de localización para tuberías metálicas basado en técnicas probabilísticas, utilizando el modelo de propagación de la señal como un mapa de radiofrecuencia. Posteriormente, se aborda la localización en túneles siguiendo una estrategia similar de reaprovechar la naturaleza periódica de la señal y se presenta un método de localización discreta. Yendo un paso más allá, y con el objetivo de mejorar la localización a lo largo del túnel incluyendo otras fuentes de información, se desarrolla un método inspirado en el paradigma del graph-SLAM donde se incorporan los resultados obtenidos de la detección de características discretas proporcionadas por el propio túnel.Para ello, se implementa un sistema de detección que proporciona la posición absoluta de características relevantes de la señal periódica radio. Del mismo modo, se desarrolla un método de detección de características estructurales del túnel (galerías) que devuelve la posición conocida de las mismas. Todos estos resultados se incorporan al grafo como fuentes de información.Los métodos de localización desarrollados a lo largo de la tesis han sido validados con datos recolectados durante experimentos llevados a cabo con plataformas robóticas en escenarios reales: la tubería de Santa Ana en Castillonroy y el túnel ferroviario de Somport.<br /

Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework; 35214292

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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland