Robot mapping and localisation in metal water pipes using hydrophone induced vibration and map alignment by dynamic time warping

Water is a highly valuable resource so asset management of associated infrastructure is of critical importance. Water distribution pipe networks are usually buried, and so are difficult to access. Robots are therefore appealing for performing inspection and detecting damage to target repairs. However, robot mapping and localisation of buried water pipes has not been widely investigated to date, and is challenging because pipes tend to be relatively featureless. In this paper we propose a mapping and localisation algorithm for metal water pipes with two key novelties: the development of a new type of map based on hydrophone induced vibration signals of metal pipes, and a mapping algorithm based on spatial warping and averaging of dead reckoning signals used to calibrate the map (using dynamic time warping). Localisation is performed using both terrain-based extended Kalman filtering and also particle filtering. We successfully demonstrate and evaluate the approach on a combination of experimental and simulation data, showing improved localisation compared to dead reckoning

Robot Mapping and Localisation in Water Pipes

The demand for inspection and repair technologies for the water industries on their water mains and distribution pipes is increasing. In urban water distribution systems, due to the fact that water pipes are ageing, pipe leakages and serious damage may occur. Compared with the cost of pipe replacement in the underground distribution system, regular pipe inspection and repair is more cost effective to water companies and local communities. However, small-diameter pipes are not accessible to humans because they are small in size and often buried underground. Therefore, inspection robotic systems are more suited to this task in terms of underground pipe networks mapping and damage localisation, in order to target repair from above ground. There are a number of challenges for robot mapping and localisation in water pipes, which are: 1) feature sparsity in water pipes – lack of navigation landmarks, 2) in-pipe robot can only detect nearby features, and 3) unlike indoor/outdoor SLAM problems, in-pipe robot has less movement flexibility. The main aim of this thesis is to solve these challenges and address the problem of robot mapping and localisation in small-diameter feature-sparse water pipes. This thesis presents a number of novel contributions. Firstly, for the front end, where raw sensor data is transformed into signals useful for mapping and localisation algorithms, new types of maps are developed here for water pipes: for plastic pipes, ultrasound data is used to map the ground profile through the plastic pipe wall, whilst for metal pipes a hydrophone is used to determine a map based on pipe vibration amplitude over space. Secondly, a new sequential approach to mapping and localisation is developed, based on alignment of multiple maps based on dynamic time warping averaging. Thirdly, a new simultaneous localisation and mapping algorithm is developed, which overcomes the limitation of the sequential approach in that the map is not updated. Finally, a new sensor fusion algorithm is developed that transforms the robot location in the local coordinate frame to the world coordinate frame, which would be essential for targeting repairs from above ground

PipeSLAM: Simultaneous Localisation and Mapping in Feature Sparse Water Pipes using the Rao-Blackwellised Particle Filter

Water, a valuable resource, is usually distributed through urban environments by buried pipes. These pipes are difficult to access for inspection, maintenance and repair. This makes in-pipe robots an appealing technology for inspecting water pipes and localising damage prior to repair from above ground. Accurate localisation of damage is of critical importance because of the costs associated with excavating roads, disrupting traffic and disrupting the water supply. The problem is that pipes tend to be relatively featureless making robot localisation a challenging problem. In this paper we propose a novel simultaneous localisation and mapping (SLAM) algorithm for metal water pipes. The approach we take is to excite pipe vibration with a hydrophone (sound induced vibration), which leads to a map of pipe vibration amplitude over space. We then develop a SLAM algorithm that makes use of this new type of map, where the estimation method is based on the Rao-Blackwellised particle filter (RBPF), termed PipeSLAM. The approach is also suited to SLAM in plastic water pipes using a similar type of map derived from ultrasonic sensing. We successfully demonstrate the feasibility of the approach using a combination of experimental and simulation data

Robot localization in water pipes using acoustic signals and pose graph optimization

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

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