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Institute for Engineering Research (I3E), Miguel Hernandez University, Avenida de la Universidad, s/, 03202, Elche, Alicante, Spain

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4 Papers

Place Recognition Using Bag of Semantic and Visual Words from Equirectangular Images

Topics: Image Processing; Vision, Recognition and Reconstruction; Guidance, Localization, Navigation and Control

Authors: María Flores , Marc Fabregat-Jaén , Juan Cabrera , Adrián Peidró , David Valiente and Luis Payá

Affiliation: Engineering Research Institute of Elche (I3E), Miguel Hernández University, Avda. de la Universidad, s/, 03202, Elche, Alicante, Spain

Digital Object Identifier: 10.5220/0013838000003982

Proceedings of ICINCO 2025, ISBN: 978-989-758-770-2

Quick Abstract

Place recognition has a crucial relevance in some tasks of mobile robot navigation. For example, it is used for the detection of loop-closure or for estimating the position of a mobile robot along a route in a known environment. If place recognition is based on visual information, it can be approached as an image retrieval problem. The Bag of Visual Words technique can be used for image retrieval. Image retrieval is based on an image representation (for example, a vector) that contains relevant visual information. In this paper, two image signatures are proposed. Both are based on semantic and visual information. A bag of visual words is created for each semantic class. Local feature descriptors are classified according to the projection of their associated point on a segmented semantic map. On the one hand, the image signature is composed of a set of histograms where each cell encodes the frequency with which a visual word appears in the image. On the other hand, the image signature is composed of a set of vectors where each cell encodes the sum of the cosine distance between the visual word and the nearest extracted features.

Redundancy Resolution in Multiple Feasibility Maps via MultiFM-RRT

Topics: Guidance, Localization, Navigation and Control; Task and Motion Planning

Authors: Marc Fabregat-Jaén ¹ , Adrián Peidró ¹ , María Flores ¹ , Luis Payá ^{1

;

2} and Óscar Reinoso ^{2

;

1}

Affiliations: ¹ Instituto de Investigación en Ingeniería de Elche, Universidad Miguel Hernández, Avda. Universidad s/n, Elche, Spain , ² ValgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, Valencia, Spain

Digital Object Identifier: 10.5220/0013835700003982

Proceedings of ICINCO 2025, ISBN: 978-989-758-770-2

Quick Abstract

This paper presents MultiFM-RRT, a novel algorithm for redundancy resolution in kinematically redundant manipulators based on the exploration of multiple Feasibility Maps (FMs). They encode all valid configurations of redundant variables for a prescribed task trajectory, enabling global optimization and constraint satisfaction. Unlike traditional velocity-based methods, which are limited to local solutions, and grid-based methods, which are computationally intensive, MultiFM-RRT leverages the Rapidly-exploring Random Tree (RRT) framework to efficiently explore the space of feasible solutions across multiple feasibility maps. The algorithm incorporates singularity maps to enable transitions between different aspects, ensuring comprehensive coverage of the solution space. By computing feasibility maps online and guiding exploration with probabilistic sampling of goal and singularity sets, MultiFM-RRT achieves a balance between computational efficiency and global optimality. The proposed approach is demonstrated on a Stewart parallel platform, illustrating its ability to generate feasible, constraint-satisfying trajectories while efficiently handling redundancy.

Augmented Feasibility Maps: A Simultaneous Approach to Redundancy Resolution and Path Planning

Topics: Guidance, Navigation and Control; Engineering Applications on Intelligent Control Systems and Optimization

Authors: Marc Fabregat-Jaén ¹ , Adrián Peidró ¹ , Esther González-Amorós ¹ , María Flores ¹ and Óscar Reinoso ^{2

;

1}

Digital Object Identifier: 10.5220/0012946000003822

Proceedings of ICINCO 2024, ISBN: 978-989-758-717-7

Quick Abstract

Redundant robotic manipulators are capable of performing complex tasks with an unprecedented level of dexterity and precision. However, their redundancy also introduces significant computational challenges, particularly in the realms of redundancy resolution and path planning. This paper introduces a novel approach to simultaneously address these challenges through the concept of Augmented Feasibility Maps, by integrating task coordinates as decision variables into the traditional feasibility maps. We validate the AFM concept by using Rapidly-Exploring Random Trees to explore the maps, demonstrating its efficacy in simulations of various dimensionalities. The method is capable of incorporating kinematic constraints, such as obstacle avoidance while adhering to joint limits.

Evaluation of Open-Source OCR Libraries for Scene Text Recognition in the Presence of Fisheye Distortion

Topics: Image Processing; Vision, Recognition and Reconstruction

Authors: María Flores , David Valiente , Marcos Alfaro , Marc Fabregat-Jaén and Luis Payá

Affiliation: Institute for Engineering Research (I3E), Miguel Hernandez University, Avenida de la Universidad, s/, 03202, Elche, Alicante, Spain

Digital Object Identifier: 10.5220/0012927600003822

Proceedings of ICINCO 2024, ISBN: 978-989-758-717-7

Quick Abstract

Due to the rich and precise semantic information that text provides, scene text recognition is relevant in a wide range of vision-based applications. In recent years, the use of vision systems that combine a camera and a fisheye lens is common in a variety of applications. The addition of a fisheye lens has the great advantage of capturing a wider field of view, but this causes a great deal of distortion, making certain tasks challenging. In many applications, such as localization or mapping for a mobile robot, the algorithms work directly with fisheye images (i.e. distortion is not corrected). For this reason, the principal objective of this work is to study the effectiveness of some OCR (Optical Character Recognition) open-source libraries applied to images with fisheye distortion. Since no scene text dataset of this kind of image has been found, this work also generates a synthetic image dataset. A fisheye model which varies some parameters is applied to standard images of a benchmark scene text dataset to generate the proposed dataset.