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, self-localising 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 multi-robot SLAM in the pipe environment.

Redundant and non-operational buildings at nuclear sites are decommissioned over a period of time. The process involves demolition of physical infrastructure resulting in large quantities of residual waste material. The resulting waste materials are packed into import containers to be delivered for post-processing, containing either sealed canisters or assortments of miscellaneous objects. At present post-processing does not happen within the United Kingdom. Sellafield Ltd. and National Nuclear Laboratory are developing a process for future operation so that upon an initial inspection, imported waste materials undergo two stages of post-processing before being packed into export containers, namely sort and segregate or sort and disrupt. The post-processing facility will remotely treat and export a wide range of wastes before downstream encapsulation. Certain wastes require additional treatment, such as disruption, before export to ensure suitability for long-term disposal. This article focuses on the design, development, and demonstration of a reconfigurable rational agent-based robotic system that aims to highly automate these processes removing the need for close human supervision. The proposed system is being demonstrated through a downsized, lab-based setup incorporating a small-scale robotic arm, a time-of-flight camera, and high-level rational agent-based decision making and control framework.

This work proposes a novel visual Simultaneous Localisation and Mapping (vSLAM) approach for robots in sewer pipe networks. One problem of vSLAM in pipes is that the scale drifts and accuracy degrades. We propose the use of structural information to mitigate this problem via cylindrical regularity. The main novelty consists of an approach for cylinder detection that is more robust than previous methods in non-smooth sewer pipe environments. Cylindrical regularity is then incorporated into both local bundle adjustment and pose graph optimisation. The approach adopts a minimal cylinder representation with only five parameters, avoiding constraints during the optimisation in vSLAM. A further novelty is that the estimated cylinder is part of the scale drift estimation, which enables a correction to the translation estimate and this further improves the accuracy. The approach, termed Cylindrical Regularity ORB-SLAM (CRORB), is benchmarked and compared to leading visual SLAM algorithms ORB-SLAM2 and direct sparse odometry (DSO), as well as a vSLAM algorithm with cylindrical regularity developed for gas pipes, using real sewer pipe data and synthetic data generated with the Gazebo modelling software. The results demonstrate that CRORB improves substantially over the competitors, with a reduction of approximately 70% in error on real data.