A novel mobile robot localization approach based on classification with rejection option using computer vision

Marinho, Leandro Balby; Filho, Pedro Pedrosa Rebouças; Almeida, Jefferson S.; Souza, João Wellington M.; Júnior, Amauri Holanda de Souza; Albuquerque, Victor Hugo C. de

doi:10.1016/j.compeleceng.2018.03.047

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…Hence, the proposed scheme is liable to be faster than Zhang et al 24 Figure 11(a) suggests that the proposed method follows the shortest path as compared to Li et al 33 Learning-based approaches are presented to localize and navigate a mobile robot in refs. [25][26][27][28][29][30]. In contrast to these work, this paper provides a unique solution to achieve both estimation (all three body-to-camera parameters) and robot control in a single loop.…”

Section: Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Sharma¹,

Shukla²,

Behera³

et al. 2019

Robotica

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SUMMARYIn this paper, we present and implement a novel approach for position-based visual servoing. The challenge of controlling the mobile robot while simultaneously estimating the camera to mobile robot transformation is solved. This is achieved using gradient descent (GD)-based estimation and the sliding-mode approach. The GD approach allows online parameter estimation for controlling the robot to achieve a desired position and orientation. The adaptive nature of the parameters demonstrates the robustness of the system. In contrast to existing work, the proposed technique achieves both estimation and control tasks in a single experiment. Simulation and experimental results are provided to validate the performance of the proposed scheme.

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Section: Comparisonmentioning

confidence: 99%

“…Machine learning-based approaches for mobile robot localization are presented in refs. [25][26][27][28][29][30]. Marinho et al 26 present an approach to localize the mobile robot via a classifier with reject option.…”

Section: Introductionmentioning

confidence: 99%

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Sharma¹,

Shukla²,

Behera³

et al. 2019

Robotica

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“…Na literatura, existem algumas técnicas bem conhecidas para descrever um espaço de trabalho, como Grade de Ocupação (Kwon et al, 2019;Jo et al, 2018;Lau et al, 2013;Singha et al, 2018), ou ainda uma abordagem Topológica (Marinho et al, 2018;Tang et al, 2019;de Meyer et al, 1997;Blochliger et al, 2018).…”

Section: Representação Do Cenáriounclassified

Manuseio e Transporte de Objetos por um Robô Terrestre

Quemelli

Brandão

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This paper aims handling box-shape object combining mapping, searching and path planning techniques. The proposal enables a mobile robot to push an object autonomously from random positions to a predefined destination. Laser scanner data are used to build up a 2D map, which aids the object's identification in the scene. Next, a Bézier curve provides suitable paths taking into account the position of the robot, the object and the final destination. Finally, numerical experiments run in V-REP + Matlab validate the proposal, which demonstrates quite efficient in several structured scenarios. Resumo: Este artigo visa o manuseio de um objeto com o formato de caixa, combinando técnicas de mapeamento, busca e planejamento de caminho. A proposta habilita um robô móvel empurrar um objeto autonomamente a partir de posições aleatórias até um destino desejado. São utilizados dados de um escâner laser para construir um mapa 2D, o qual ajuda na identificação do objeto na cena. Em seguida, uma curva de Bézier define um caminho adequado levando em consideração a posição do robô, do objeto e o destino desejado. Por fim, são realizados experimentos numéricos em V-REP + Matlab para validar a proposta, a qual demonstrou-se bastante eficiente em diferentes cenários estruturados.

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“…Texture classification is the process of categorization based on its unique characteristics [1], [2]. Applications of texture classification include: detection of surface defects [3], [4], identification of tissues in tomography images [5]- [7], robotic vision [8]- [11], analysis of sonar imagery [12], recognition of facial expressions [13], [14], detection of a moving object [15], [16]. The performance of The associate editor coordinating the review of this manuscript and approving it for publication was Wenming Cao.…”

Section: Introductionmentioning

confidence: 99%

“…If the features are not unique to the texture, accurate classification performance cannot be achieved [1]. The properties for a desirable feature vector are as follows: The features should be (1) discriminating [17]- [20], (2) noise robust [8], [9], (3) invariant to image orientation and lighting changes [2], [21], [22], (4) smaller in dimension [23], [24], and (5) efficient in implementation [25], [38]. The proposed work mainly focuses on the performance of texture classification in the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

et al. 2019

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A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.

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A novel mobile robot localization approach based on classification with rejection option using computer vision

Cited by 24 publications

References 18 publications

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Manuseio e Transporte de Objetos por um Robô Terrestre

Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

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