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.

In this work, we propose a 3D frontier-based exploration solution for aerial robots, focused on indoor and closed environments. Our approach provides fast and exhaustive frontier detection by expanding two trees, one local and other global, using a custom RRT algorithm. Furthermore, to enhance the expansion of the global tree, which is also used to compute paths through the environment, key branches of the local tree are joined to this structure. Moreover, in contrast to other solutions that make use of separate local and global planning processes, our strategy relies on a single-plan strategy, which permits involving frontiers found at both levels by the decision making process. Finally, to demonstrate the effectiveness of our solution, we compare its performance with GBPlanner2 in a challenging environment by using a simulated aerial robot.

SLAM approaches rely on loop closure strategies to correct the inconsistencies of the generated map. These inconsistencies are mainly caused by the effect of sensor noise in odometry sources. For the case of visual SLAM, loop detection typically rely on the repetitive detection and matching of texture-based keypoints. Weakly textured environments, however, can lead to scenes lacking these kind of points and, hence, poor-performing loop detectors. An alternative for these environments is the use of geometrical cues such as line segments, which are frequently present within human-made, structured environments. Under this context, in this work, we introduce a novel appearance-based loop closure detection method that integrates lines and points to enhance performance in these scenarios. For this purpose, we build an incremental Bag-of-Binary-Words scheme for each visual cue to retrieve previously seen images from the two complementary perspectives. Furthermore, we rely on a late fusion strategy to combine the image candidates resulting for both visual vocabularies. An effective mechanism to group similar images close in time is applied next to reduce the effort of the image candidate search. Finally, we propose a novel scheme to validate geometrically the loop candidates, integrating lines into the procedure. The proposed approach compares favourably with other state-of-the-art methods for several datasets.

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.

Recognizing whether the current place has been visited before or not is an important task in robotic navigation, since it helps to reduce the inconsistencies produced by the inherent noise of navigation sensors. When a camera is used as input for navigation, this process is known as visual loop closure detection. Under this context, in this work we propose a loop closure detection method based on superpixels and a Bag of Words scheme. A novel image description method for superpixels is proposed. Our approach also makes use of the concept of dynamic islands, which allows us to group images close in time and to avoid images taken from the same place to compete among them as loop closure candidates. The proposed method is validated using several outdoor public image sequences and compared to other state-of-the-art solutions.

In this paper, we propose a novel appearance-based approach for topological mapping based on a hierarchical decomposition of the environment. In our map, images with similar visual properties are grouped together in nodes, which are represented by means of an average global descriptor and an index of binary features based on a bag-of-words online approach. Each image is represented by means of a global descriptor and a set of local features, and this information is used in a two-level loop closure approach, where first global descriptors are employed to obtain the most likely nodes of the map and then binary image features are used to retrieve the most likely images inside these nodes. This hierarchical scheme enables us to reduce the search space when recognizing places, maintaining high accuracy when creating a map. Our approach is validated using several public datasets and compared against several state-of-the-art techniques. The accuracy and the sparsity of the generated maps are also discussed.

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.

We propose an appearance-based loop closure detection algorithm based on binary features and a Bag-of-Words scheme. Unlike other approaches that build the visual dictionary offline, we introduce an indexing method for binary features, which, in combination with an inverted index, enable us to obtain loop closure candidates in an online manner. These structures are used in a discrete Bayes filter to select final loop candidates and to ensure temporal coherency between predictions. Our approach is validated using two publicly available datasets of outdoor environments and compared with the state-of-the-art FAB-MAP algorithm, showing very promising results and demonstrating that binary features can be used for visual loop closure detection.

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.