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Towards Loop Closure Detection for SLAM Applications Using Bag of Visual Features: Experiments and Simulation

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Computational Neuroscience (LAWCN 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1519))

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Abstract

This paper presents a new approach to exploring sparse and binary convolutional filters in traditional Convolutional Neural Networks (CNN). Recent advances in the integration of Deep Learning architectures, particularly in mobile autonomous robotics applications, have motivated several researches to overcome the challenges related to the limitations of computational resources. One of the biggest challenges in the area, is the development of applications to address the Loop Closure Detection problem in Simultaneous Localization and Mapping (SLAM) systems. For such application, it is necessary to use exhaustive computational power. Nevertheless, resource optimization of Convolutional Neural Network models enhances the capability of integration. Therefore, we propose the reformulation of convolutional layers through Local Binary Descriptors (LBD) to achieve this kind of optimization of CNN’s resources. This paper discusses the evaluation of a Bag of Visual Features (BoVF) approach, extracting features through local descriptors (e.g., SIFT, SURF, KAZE), and local binary descriptors (e.g., BRIEF, ORB, BRISK, AKAZE, FREAK). The descriptors were evaluated in the recognition and classification steps using six visual datasets (i.e., MNIST, JAFFE, Extended CK+, FEI, CIFAR-10, and FER-2013) through a Multilayer Perceptron (MLP) classifier. Experimentally, we demonstrated the feasibility of producing promising results by combining BoVF with MLP classifier. Additionally, we can assume that the computed descriptors generated by a Local Binary Descriptor alongside the proposed hybrid DNN (Deep Neural Network) architecture can satisfactorily accomplish the results for the optimization of a CNN’s resources applied to the Loop Closure Detection problem.

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Notes

  1. 1.

    https://github.com/whoisraibolt/BoVF-with-MLP-classifier.

  2. 2.

    https://github.com/whoisraibolt/Reformulation-of-Convolutional-Filters-through-Descriptors.

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Acknowledgments

– This work was financed in part by the CoordenaçÃo de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

– This work was carried out with the support of the Programa de CooperaçÃo Acadêmica em Defesa Nacional (PROCAD-DEFESA).

Author information

Authors and Affiliations

  1. Instituto Militar de Engenharia (IME), Praça Gen. Tibúrcio, 80 - Urca, Rio de Janeiro, RJ, Brazil

    Alexandra Miguel Raibolt da Silva, Gustavo Alves Casqueiro & Paulo Fernando Ferreira Rosa

  2. Faculdade de Ed.Tec.do Estado do Rio de Janeiro (FAETERJ/Petrópolis), Av. Getúlio Vargas, 335 - Quitandinha, Petrópolis, RJ, Brazil

    Alberto Torres Angonese

Authors
  1. Alexandra Miguel Raibolt da Silva
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  2. Gustavo Alves Casqueiro
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  3. Alberto Torres Angonese
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  4. Paulo Fernando Ferreira Rosa
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Corresponding authors

Correspondence to Alexandra Miguel Raibolt da Silva or Paulo Fernando Ferreira Rosa .

Editor information

Editors and Affiliations

  1. Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil

    Paulo Rogério de Almeida Ribeiro

  2. Federal University of São João del-Rei, São João del-Rei, Minas Gerais, Brazil

    Vinícius Rosa Cota

  3. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Dante Augusto Couto Barone

  4. Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil

    Alexandre César Muniz de Oliveira

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da Silva, A.M.R., Casqueiro, G.A., Angonese, A.T., Rosa, P.F.F. (2022). Towards Loop Closure Detection for SLAM Applications Using Bag of Visual Features: Experiments and Simulation. In: Ribeiro, P.R.d.A., Cota, V.R., Barone, D.A.C., de Oliveira, A.C.M. (eds) Computational Neuroscience. LAWCN 2021. Communications in Computer and Information Science, vol 1519. Springer, Cham. https://doi.org/10.1007/978-3-031-08443-0_3

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  • DOI: https://doi.org/10.1007/978-3-031-08443-0_3

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