CRAS

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http://localhost:4000/handle/123456789/39
This performs research and development activities in autonomous robotic systems, mobile robotics and multi-robot mobile systems for inspection, monitoring and mapping, with applications in security, energy, environment, aquaculture, oceanography, marine biology, resource extraction, among other sectors. These activities are supported by research in perception, navigation, control, localization, coordination, and automatic data acquisition and processing.

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Browsing CRAS by Author "6868"
  • Item
    Comparative Study of Visual Odometry and SLAM Techniques
    ( 2018) Alexandra Nunes ; Matos,A ; Andry Maykol Pinto ; Ana Gaspar ; 5446 ; 6869 ; 6868
    The use of the odometry and SLAM visual methods in autonomous vehicles has been growing. Optical sensors provide valuable information from the scenario that enhance the navigation of autonomous vehicles. Although several visual techniques are already available in the literature, their performance could be significantly affected by the scene captured by the optical sensor. In this context, this paper presents a comparative analysis of three monocular visual odometry methods and three stereo SLAM techniques. The advantages, particularities and performance of each technique are discussed, to provide information that is relevant for the development of new research and novel robotic applications. © Springer International Publishing AG 2018.
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    Critical object recognition in underwater environment
    ( 2019) Alexandra Nunes ; Ana Gaspar ; Aníbal Matos ; 5158 ; 6868 ; 6869
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    Evaluation of Bags of Binary Words for Place Recognition in Challenging Scenarios
    ( 2021) Ana Gaspar ; Alexandra Nunes ; Aníbal Matos ; 5158 ; 6868 ; 6869
    To perform autonomous tasks, robots in real-world environments must be able to navigate in dynamic and unknown spaces. To do so, they must recognize previously seen places to compensate for accumulated positional deviations. This task requires effective identification of recovered landmarks to produce a consistent map, and the use of binary descriptors is increasing, especially because of their compact representation. The visual Bag-of-Words (BoW) algorithm is one of the most commonly used techniques to perform appearance-based loop closure detection quickly and robustly. Therefore, this paper presents a behavioral evaluation of a conventional BoW scheme based on Oriented FAST and Rotated BRIEF (ORB) features for image similarity detection in challenging scenarios. For each scenario, full-indexing vocabularies are created to model the operating environment and evaluate the performance for recognizing previously seen places similar to online approaches. Experiments were conducted on multiple public datasets containing scene changes, perceptual aliasing conditions, or dynamic elements. The Bag of Binary Words technique shows a good balance to deal with such severe conditions at a low computational cost. © 2021 IEEE.
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    A mosaicking technique for object identification in underwater environments
    ( 2019) Alexandra Nunes ; Ana Gaspar ; Andry Maykol Pinto ; Aníbal Matos ; 5446 ; 6869 ; 6868 ; 5158
    Purpose: This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition. Design/methodology/approach: This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic. Findings: A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers. Originality/value: The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information. © 2018, Emerald Publishing Limited.
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    Occupancy Grid Mapping from 2D SONAR Data for Underwater Scenes
    ( 2021) Alexandra Nunes ; Ana Gaspar ; Aníbal Matos ; 5158 ; 6868 ; 6869
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    Simultaneous Underwater Navigation and Mapping
    ( 2019) Aníbal Matos ; Ana Gaspar ; 5158 ; 6868
    The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.
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    Urban@CRAS dataset: Benchmarking of visual odometry and SLAM techniques
    ( 2018) Ana Gaspar ; Aníbal Matos ; Andry Maykol Pinto ; Alexandra Nunes ; 6868 ; 5446 ; 5158 ; 6869