The use of robots to inspect large infrastructures is now a reality. They allow thorough assessments of the condition of such structures, unaffected by fatigue or lack of concentration, minimizing operator risk and reducing costs. In addition, robotic inspection ensures complete structure coverage. An example is MUSSOL, an aerial platform designed for inspecting the metallic structures of large vessels, developed and tested in various research projects. Its control architecture suits inspection tasks well, providing autonomous missions for long or repetitive tasks through a list of sequential way-points. However, the lack of a specific trajectory can compromise inspection coverage. This paper presents a trajectory follower for our aerial platform to ensure straight-line movements to way-points. The method is easily configurable, and results from simulations and real experiments demonstrate its effectiveness, even in windy conditions.

The periodic inspection of vessels is a fundamental task to ensure their integrity and avoid maritime accidents. Currently, these inspections represent a high cost for the ship owner, in addition to the danger that this kind of hostile environment entails for the surveyors. In these situations, robotic platforms turn out to be useful not only for safety reasons, but also to reduce vessel downtimes and simplify the inspection procedures. Under this context, in this paper we report on the evaluation of a new control architecture devised to drive an aerial platform during these inspection procedures. The control architecture, based on an extensive use of behaviour-based high-level control, implements visual inspection-oriented functionalities, while releases the operator from the complexities of inspection flights and ensures the integrity of the platform. Apart from the control software, the full system comprises a multi-rotor platform equipped with a suitable set of sensors to permit teleporting the surveyor to the areas that need inspection. The paper provides an extensive set of testing results in different scenarios, under different operational conditions and over real vessels, in order to demonstrate the suitability of the platform for this kind of tasks.

Process automation is enabling a level of accuracy and productivity that goes beyond human ability, and one critical area where automation is making a big difference is quality control. In this paper, we describe a semantic segmentation solution aiming at detecting the presence of quality control elements in surgery toolboxes prepared by the sterilization unit of a hospital. In order to reduce the time required to prepare pixel-level ground truth, this work focuses on the use of weakly-supervised annotations (scribbles). Moreover, our solution integrates a clustering approach into a semantic segmentation network, thereby reducing the negative effects caused by weakly-supervised annotations. The paper describes the design process and reports on the results obtained.

Vessels are nowadays widely used for goods transportation. To ensure their physical integrity, they must be periodically surveyed, searching for defects, e.g. cracks and corrosion. The inspection process is carried out at a great cost due to the arrangements required to allow the surveyors to reach any point of the structure, while guaranteeing their safety. In recent years, the idea of introducing robots in the inspection of ships is being investigated. In this line, we propose two contributions for the estimation of 3D position inside vessels, making use of UWB technology. Since this technology is typically affected by the presence of metallic elements, we firstly propose a filtering method to improve the tag-anchor distance estimates. The second contribution consists in a novel method based on ICP to estimate the position of the UWB tag making use of point-to-sphere correspondence. Both contributions have been evaluated using several datasets taken inside a real vessel. The results show that our methods are able to provide satisfactory position estimates inside that environment.

Following the success of machine vision systems for on-line automated quality and process control, in this paper we describe an object recognition solution aiming at detecting the presence of quality control elements in surgery toolboxes prepared by the Sterilization Unit of a hospital. Our solution actually consists in a two-stage arbitrarily-oriented object detection method making use of indirect regression of oriented bounding boxes parameters. The paper describes the design process and reports on the results obtained up to date.

Seagoing vessels have to undergo regular visual inspections in order to detect defects such as cracks and corrosion before they result into catastrophic consequences. These inspections are currently performed manually by ship surveyors at a great cost, so that any level of assistance during the inspection process would significatively decrease the inspection cost. In this paper, we describe a novel framework for visually detecting the aforementioned defects. This framework is generic and flexible in the sense that it can be easily configured to compute the features that perform better for the inspection at hand. In this regard, inspired by the idea of conspicuity, this work considers contrast and symmetry as features for detecting defects on vessel structures. A combination operator is additionally tested in order to merge the information provided by these features and improve the detection performance. Experimental results for the different configurations of the detection framework show better classification results than state of the art methods.

Image mosaicing has gained increasing attention in the last few years, specially for robotic mapping applications. Due to the richness of the sensor data provided, several science fields require the creation of large-area image mosaics for further analysis. In this paper, we propose a novel and generic image mosaicing approach that can produce seamless composites under different configurations in a reasonable amount of time. Our approach is based on a multi-threaded architecture which allows us to execute the different steps of the algorithm simultaneously. To find the topology of the environment, we use a visual index based on a Bag-of-Binary-Words scheme, which is built in an online manner, and thus avoids the classic training step. Our approach is validated under different environments and camera configurations, showing that it can be used on several scenarios producing coherent results in all of them. Furthermore, the implementation of the algorithm is made public to the community.

Seagoing vessels have to undergo regular visual inspections in order to detect the typical defective situations affecting metallic structures, such as cracks and corrosion. These inspections are currently performed by ship surveyors manually at a great cost. To make ship inspections safer and more cost-efficient, this paper presents a Micro-Aerial Vehicle (MAV) intended for visual inspection and based on supervised autonomy. On the one hand, the vehicle is equipped with a vision system that effectively teleports the surveyor from the base station to the areas of the hull that need inspection. On the other hand, the MAV is the result of a complete redesign of a visual inspection-oriented aerial platform that we proposed some years ago, with the aim of introducing the surveyor in the control loop and, in this way, enlarge the range of inspection operations that can robustly be carried out. Another goal is to make the platform as usable as possible for a non-expert. All this has been accomplished by means of the definition of different autonomous functions, including obstacle detection and collision prevention, and extensive use of behavior-based high-level control. The results of some experiments conducted to assess both the performance and usability of the platform are discussed at the end of the paper.

Vessel maintenance entails periodic visual inspections of internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures. Nowadays, robots are becoming more and more important regarding these inspection tasks, since they can collect the requested information and, thus, prevent humans from performing tedious, and even dangerous tasks because of places hard to reach for humans. A Micro Aerial Vehicle (MAV) fitted with vision cameras can be used as part of an automated or semi-automated inspection strategy. The resulting collection of individual images, however, does not permit the surveyor to get a global overview of the state of the surface under inspection, apart from the fact that the defects can be separated into different consecutive images. Image mosaicing can certainly help in this case. To this end, in this paper, we propose a novel image mosaicing approach able to deal with this kind of scenarios. Our solution employs a graph-based registration method from which relevant topological relationships between (overlapping) images are found. This graph is built according to a visual index based on a Bag-of-Words (BoW) scheme making use of binary descriptors for speeding up the image description process. At the end of the paper, we report about the results of a number of experiments that validate our approach, including the outcome of defect detectors working directly over the mosaic.

The use of saliency mechanisms for defect detection is discussed in this work. We consider defects on regular surfaces as conspicuous areas that catch the attention of the surveyors. Following this approach, we propose the use of the Bayesian framework SUN, devised to provide saliency information based on natural statistics, to combine information about the visual appearance of the surface under inspection, to finally indicate where the defects (if any) are located. The visual information is suggested to be based on features commonly used to predict human eye fixations: contrast and symmetry. We demonstrate that these two features provide a description of the surface that can be used to indicate whether it is defective or not. Our approach is assessed using a publicly available image dataset containing a variety of surfaces with defective areas. The performance of the defect detector is evaluated through cross-validation and successful results are obtained.

Vessel maintenance entails periodic visual inspections of internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures, such as cracks, coating breakdown, corrosion, etc. The main goal of the EU-FP7 project MINOAS is the automation of the inspection process, currently undertaken by human surveyors, by means of a fleet of robotic agents. This paper overviews a semi-autonomous approach to the inspection problem consisting of an autonomous Micro Aerial Vehicle (MAV) to be used as part of this fleet and which is in charge of regularly supplying images that can effectively teleport the surveyor from a base station to the areas of the hull to be inspected. Specific image processing software to analyze those images and assist the surveyor during the repair/no repair decision making process is also contributed. The control software approach adopted for the MAV, including self-localization and obstacle avoidance, is described and discussed, and experimental results in this regard are as well reported.

Periodic visual inspection of the internal and external parts of vessels hull is typically performed by trained surveyors at great cost. Assisting them during the inspection process by means of mechanisms capable of defect detection would certainly decrease inspection cost. With this aim, this paper presents a corrosion detection algorithm built around a weak classifiers cascade scheme and reports on its performance. As a secondary contribution, a crack detection approach guided by the output of the corrosion detector is also proposed. As a result, false positives rate is reduced as well as the computation time.

Vessel maintenance entails periodic visual inspections of internal and external parts of the vessel hull in order to detect the typical defective situations affecting metallic structures, such as cracks, corrosion, etc. This paper presents the first steps towards a roboticized visual inspection system aiming at automating as much as possible the whole inspection process currently undertaken by human surveyors. Occurrence of defects are first outlined, followed by a brief description of the whole system. Next, details for one of the robotic platforms to be used are given, as well as for the image processing algorithms dealing with the visual detection of defects.