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.

Camera trajectory estimation has generated a lot of interest during the last decades, especially for robotic positioning. As it is well-known, outdoors positioning mainly relies on GPS, whereas visual odometry is typically preferred indoors. Nevertheless, when the environment is weakly textured, visual odometers typically degrade in performance. Facing this situation, this paper develops and tests a novel visual odometer that combines image edges and planar information to estimate the trajectory of an RGB-D camera in environments that lack texture. We also present a plane matching method based on a graph matching technique. To conclude, a comparison involving the proposed odometer for two well-known datasets and other visual odometers and SLAM systems is reported. This comparison shows that our method outperforms the others for indoor poorly-textured environments, while similar performance is obtained for indoor highly-textured environments.

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.