Alberto Ortiz

University of the Balearic Islands, Palma, Balearic Islands, Spain

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Publications (79)19.95 Total impact

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    ABSTRACT: 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.
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    ABSTRACT: 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 typically defects appear broken along a number of 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.
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: Topological maps model the environment as a graph, where nodes are distinctive places of the environment and edges indicate topological relationships between them. They represent an interesting alternative to the classic metric maps, due to their simplicity and storage needs, what has made topological mapping and localization an active research area. The different solutions that have been proposed during years have been designed around several kinds of sensors. However, in the last decades, vision approaches have emerged because of the technology improvements and the amount of useful information that a camera can provide. In this paper, we review the main solutions presented in the last fifteen years, and classify them in accordance to the kind of image descriptor employed. Advantages and disadvantages of each approach are thoroughly reviewed and discussed.
    Robotics and Autonomous Systems 11/2014; 64. DOI:10.1016/j.robot.2014.11.009 · 1.11 Impact Factor
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: We propose an appearance-based loop closure detec-tion algorithm based on binary features and a Bag-of-Words scheme. Unlike other approaches that build the vi-sual dictionary offline, we introduce an indexing method for binary features, which, in combination with an in-verted index, enable us to obtain loop closure candidates in an online manner. These structures are used in a dis-crete Bayes filter to select final loop candidates and to ensure temporal coherency between predictions. Our ap-proach is validated using two publicly available datasets of outdoor environments and compared with the state-of-the-art FAB-MAP algorithm, showing very promising re-sults and demonstrating that binary features can be used for visual loop closure detection.
    IEEE International Conference on Emerging Technologies and Factory Automation (ETFA); 09/2014
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    ABSTRACT: Vessel maintenance entails periodic visual inspections of the internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures, such as coating breakdown, corrosion, cracks, etc. The main goal of 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 an approach to the inspection problem based on an autonomous Micro Aerial Vehicle (MAV) which, as part of this fleet, is in charge of regularly supplying images that can teleport the surveyor from a base station to the areas of the hull to be inspected. The control software approach adopted for the MAV is fully described, with a special emphasis on the self-localization capabilities of the vehicle. Experimental results showing the suitability of the platform to the application are as well reported and discussed.
    Journal of Intelligent and Robotic Systems 09/2014; DOI:10.1007/s10846-013-9852-4 · 0.81 Impact Factor
  • Francisco Bonnin-Pascual · Alberto Ortiz
    XIX IEEE International Conference on Emerging Technologies and Factory automation (ETFA), Barcelona; 09/2014
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: We propose an appearance-based approach for topological visual mapping and localization using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of randomized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. We assess our approach with several datasets obtained from indoor and outdoor environments under different weather conditions.
    euRathlon/ARCAS Workshop on Field Robotics, Seville (Spain); 06/2014
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    ABSTRACT: Seagoing vessels have to undergo regular inspections, which are currently performed manually by ship surveyors. The main cost factor in a ship inspection is to provide access to the different areas of the ship, since the surveyor has to be close to the inspected parts, usually within arm's reach, either to perform a visual analysis or to take thickness measurements. The access to the structural elements in cargo holds, e.g., bulkheads, is normally provided by staging or by “cherry-picking” cranes. To make ship inspections safer and more cost-efficient, we have introduced new inspection methods, tools, and systems, which have been evaluated in field trials, particularly focusing on cargo holds. More precisely, two magnetic climbing robots and a micro-aerial vehicle, which are able to assist the surveyor during the inspection, are introduced. Since localization of inspection data is mandatory for the surveyor, we also introduce an external localization system that has been verified in field trials, using a climbing inspection robot. Furthermore, the inspection data collected by the robotic systems are organized and handled by a spatial content management system that enables us to compare the inspection data of one survey with those from another, as well as to document the ship inspection when the robot team is used. Image-based defect detection is addressed by proposing an integrated solution for detecting corrosion and cracks. The systems' performance is reported, as well as conclusions on their usability, all in accordance with the output of field trials performed onboard two different vessels under real inspection conditions.
    Journal of Field Robotics 03/2014; 31(2):n/a-n/a. DOI:10.1002/rob.21498 · 1.88 Impact Factor
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: We propose an appearance-based approach for topological visual mapping and localiza-tion using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of random-ized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. In order to avoid redundant information in the resulting maps, we also present a map refinement frame-work, which takes into account the visual information stored in the map for refining the final topology of the environment. These refined maps save storage space and improve the exe-cution times of localizations tasks. The approach is validated using image sequences from several environments and compared with the state-of-the-art FAB-MAP 2.0 algorithm.
    Robotica 02/2014; 33(07). DOI:10.1017/S0263574714000782 · 0.89 Impact Factor
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    Francisco Bonnin-Pascual · Alberto Ortiz
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    ABSTRACT: This dataset consists of a collection of images taken from structures and surfaces that present different kinds of defects: corrosion, cracks, coating break-down, etc. The dataset also contains a collection of B/W images which resulted from the manual labelling of the defects in the original images (black means defective area).
  • Francisco Bonnin-Pascual · Alberto Ortiz
    Developments in Corrosion Protection, Edited by Mahmood Aliofkhazraei, 01/2014: chapter Corrosion Detection for Automated Visual Inspection: pages 619-632; InTech., ISBN: 978-953-51-1223-5
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    ABSTRACT: This paper presents a novel approach to perform obstacle avoidance and robot localization using a single camera. This approach is based on the continuous detection and tracking of image features. Features are classified as ground points or obstacle points using the IPT (Inverse Perspective Transformation). Obstacle avoidance is achieved by means of a qualitative local occupancy grid built using the visually detected obstacle points, while the features classified as ground points are used to perform robocentric localization. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal can be used, and show, both qualitatively and quantitatively, the benefits it provides.
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    ABSTRACT: Navigating along a set of programmed points in a completely unknown environment is a challenging task which mostly depends on the way the robot perceives and symbolizes the environment and decisions it takes in order to avoid the obstacles while it intends to reach subsequent goals. Tenacity and Traversability (T-2)(1)-based strategies have demonstrated to be highly effective for reactive navigation, extending the benefits of the artificial Potential Field method to complex situations, such as trapping zones or mazes. This paper presents a new approach for reactive mobile robot behavior control which rules the actions to be performed to avoid unexpected obstacles while the robot executes a mission between several defined sites. This new strategy combines the T-2 principles to escape from trapping zones together with additional criteria based on the Nearness Diagram (ND)(13) strategy to move in cluttered or densely occupied scenarios. Success in a complete set of experiments, using a mobile robot equipped with a single camera, shows extensive environmental conditions where the strategy can be applied.
    Robotica 07/2013; 32(4):591-609. DOI:10.1017/S0263574713000878 · 0.89 Impact Factor
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: An appearance-based approach for topological visual mapping and local-ization using local invariant features is proposed in this paper. To optimize running times, matchings between the current image and previous visited places are determined using an index based on a set of randomized KD-trees. A discrete Bayes filter is used for predicting loop candidates, whose obser-vation model is a novel approach based on an efficient matching scheme be-tween features. In order to avoid redundant information in the resulting maps, in this work, we also present a map refinement framework, which takes into account the visual information stored in the map for refining the final topol-ogy of the environment. These refined maps save storage space and improve the execution times of localizations tasks. The approach has been validated using image sequences from several environments.
  • Emilio Garcia-Fidalgo · Alberto Ortiz
    [Show abstract] [Hide abstract]
    ABSTRACT: An appearance-based approach for visual mapping and local- ization is proposed in this paper.On the one hand, a new image similarity measure between images based on number of matchings and their asso- ciated distances is introduced. On the other hand, to optimize running times, matchings between the current image and previous visited places are determined using an index based on a set of randomized KD-trees. Further, a discrete Bayes filter is used for predicting loop candidates, tak- ing into account the previous relationships between visual locations. The approach has been validated using image sequences from several envi- ronments. Whereas most other approaches use omnidirectional cameras, a single-view configuration has been selected for our experiments.
    VI Iberian Conference on Pattern Recognition and Image Analysis, Funchal (Portugal); 06/2013
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    Emilio Garcia-Fidalgo · Alberto Ortiz
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    ABSTRACT: Visual loop closure detection in robotics is dened as the ability of recognizing previously seen places given the current image captured by the robot. The Bag-of-Words image representation has been widely used for these kinds of tasks. However, in this paper, an appearance-based approach for loop closure detection using local invariant features is proposed. Images are described using SIFT features and, for avoid-ing image-to-image comparisons, a set of randomized KD-trees are employed for feature matching. Further, a discrete Bayes lter is used for predicting loop closure candidates, whose likelihood is based on these KD-trees. The approach has been validated using monocular image sequences from several environments.
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    ABSTRACT: Vessel maintenance entails periodic visual inspec-tions of internal and external parts of the hull in order to de-tect the typical defective situations affecting metallic struc-tures, such as coating breakdown, corrosion, cracks, etc. The main goal of project MINOAS is the automation of the in-spection process, currently undertaken by human surveyors, by means of a fleet of robotic agents. This paper overviews an approach to the inspection problem based on an autono-mous Micro Aerial Vehicle (MAV) to be used as part of this fleet and which is in charge of regularly supplying images that can teleport the surveyor from a base station to the areas of the hull to be inspected. The control software approach adopted for the MAV is fully described, with a special em-phasis on the self-localization capabilities of the vehicle. Ex-perimental results showing the suitability of the platform to the application are reported and discussed.
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    ABSTRACT: 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.
    IEEE Conference on Intelligent Robots and Systems, IROS'12, Vilamoura (Portugal); 10/2012
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    ABSTRACT: Large-tonnage vessels need to be revised periodically in order to detect defective situations, such as cracks, coating breakdown or corrosion that could lead to a catastrophe. The EU-FP7 project MINOAS is designed to develop a fleet of robotic platforms to automate this inspection process. This paper presents a Micro Aerial Vehicle platform to be used as part of this fleet. The control architecture adopted for the MAV and the key challenges that have guided us towards this solution are described and discussed, while hardware, software and network congurations are also exposed. Finally, experimental results proving the suitability of the design are reported.
    III Jornadas de Computación Empotrada, Elche (Spain); 09/2012
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    ABSTRACT: This paper presents an approach to visual obstacle avoidance and reactive robot navigation for outdoor and indoor environments. The obstacle detection algorithm includes an image feature tracking procedure followed by a feature classification process based on the IPT (Inverse Perspective Transformation). The classifier discriminates obstacle points from ground points. Obstacle features permit to draw out the obstacle boundaries which are used to construct a local and qualitative polar occupancy grid, analogously to a visual sonar. The navigation task is completed with a robocentric localization algorithm to compute the robot pose by means of an EKF (Extended Kalman Filter). The filter integrates the world coordinates of the ground points and the robot position in its state vector. The visual pose estimation process is intended to correct possible drifts on the dead-reckoning data provided by the proprioceptive robot sensors. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal has proved to be useful, and show, both qualitatively and quantitatively, the benefits it provides.
    Robotica 05/2012; 31. DOI:10.1017/S0263574712000252 · 0.89 Impact Factor