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The recognition of places by using visual information in underwater environments is important when performing autonomous robotic exploration of the same area at different periods of time. It helps the robot to know its location and take decisions accordingly. However, vision-based recognition of underwater places can be a very challenging task due to the inherent properties of this kind of places...
In this work, we present a bio-inspired fin prototype as a propulsion system for a submarine robot. We explain the rajiform locomotion, which is intended to be imitated with the movements of the fin. It is also explained how the undulations on the fin allow it to have different behaviors, thus generating thrust in different directions, which in turn generates several types of motion for the fin. Our...
In this paper, we present a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination. The high similarity of features makes difficult the disambiguation between two different places. The novelty of our method relies on using the Bag of Words (BoW) approach to derive an image descriptor from a set of relevant...
For underwater robotics applications involving monitoring and inspection tasks, it is important to capture quality color images in real time. In this paper, we propose a statistically learning method with an automatic selection of the training set for restoring the color of underwater images. Our statistical model is a Markov Random Field with Belief Propagation (MRF-BP). The quality of the results...
In most artificial vision systems the quality of acquired images is directly related with the amount of information that can be obtained from them, and, particularly in underwater robotics applications involving monitoring and inspection tasks this is crucial. Statistical learning methods like Markov Random Fields with Belief Propagation (MRF-BP) provide a solution by using existing essential correlations...
Vision-based place recognition in underwater environments is a key component for autonomous robotic exploration. However, this task can be very challenging due to the inherent properties of this kind of places such as: color distortion, poor visibility, perceptual aliasing and dynamic illumination. In this paper, we present a method for vision-based place recognition in coral reefs. Our method relies...
This ongoing research work presents a servo actuated mirror-based design that allows a fixed front-view visual system mounted in an underwater vehicle to extend its field of view by controlling its gaze. We are interested in the autonomous underwater exploration of coral reefs. This type of exploration must involve a cautious and collision-free navigation to avoid damaging the marine ecosystem. Generally,...
This ongoing research focuses on providing skills based on motor-perceptual behaviors to an underwater vehicle in order to execute collision-avoidance trajectories in a more natural and intuitive way. An intuitive action can be seen as a reflex in humans and some animals. Reflexs do not involve a conscious reasoning at the time of execution, this is because a motor-perceptual skill for a particular...
In this paper we present a novel method for classifying relevant points in a sequence of images of a distant target to autonomously guide an underwater vehicle towards it. Feature points are classified by using a measure called motion perceptibility, which relates the magnitudes of the rate of change between matched feature points at different image frames (in distance), thus inherently considering...
We present a behavioral approach for autonomous robotic exploration of marine habitat with collision avoidance given little or no prior information. In our previous work, a vision-based reactive navigation paradigm with a predefined forward direction allowed an underwater robot to avoid unexpected obstacles. In this work, we have now incorporated visual perceptive invariants to guide the navigation...
Visual-based autonomous robotic exploration of unstructured and highly dynamic environments is a complex task. We present an approach to carry out an attention-driven exploration of underwater environments. This work is aimed to grant autonomy to an exploring agent in terms of deciding where to move in function of relevant visual information. This way we could obtain close video-observations of regions...
We present a visual based approach for reactive autonomous navigation of an underwater vehicle. In particular, we are interested in the exploration and continuous monitoring of coral reefs in order to diagnose disease or physical damage. An autonomous underwater vehicle needs to decide in real time the best route while avoiding collisions with fragile marine life and structure. We have opted to use...
We present a novel technique that robustly segments free-space for robot navigation purposes. In particular, we are interested in a reactive visual navigation, in which the rapid and accurate detection of free space where the robot can navigate is crucial. Contrary to existing methods that use multiple cameras in different configurations, we use a downward-facing monocular camera to search for free...
Visual landmark tracking represents a major problem due to occlusion, illumination variations and affine transformations between subsequent images. A desired goal for several mobile robot applications is to increase a steady state in the invariants in order to minimize errors in the tracking. This manuscript consists of two parts. The first part presents a quantitative analysis of stability tracking...
In Part I of this research work [1] we analysed the stability tracking of three well-known invariant descriptors (i.e., SIFT, MSER and QUICK-SHIFT). The MSER algorithm resulted to be less vulnerable than the other algorithms to fail detecting feature points. However, its instability due to sensor uncertainty and light variations was similar to other algorithms. In this paper, we now propose a novel...
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