Visual Memory Construction for Autonomous Humanoid Robot Navigation

Lopez-Martinez, Alan; Cuevas, Francisco Javier Panera; Sosa-Balderas, J. V.

doi:10.1007/978-981-32-9632-9_12

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Reference [9] presents the construction of a visual map suited for humanoid robot navigation. It proposes a genetic algorithm that estimates the epipolar geometry to dive into the image-matching problem in the construction process of a visual map.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

García-Pintos,

Aldana-Murillo,

Ovalle-Magallanes

et al. 2023

Eng

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Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to perform the aforementioned stages. In this paper, we propose a novel framework to generate a visual map for environments both indoors and outdoors. The visual map comprises key images sharing visual information between consecutive key images. This learning stage employs a pre-trained local feature transformer (LoFTR) constrained with a 3D projective transformation (a fundamental matrix) between two consecutive key images. Outliers are efficiently detected using marginalizing sample consensus (MAGSAC) while estimating the fundamental matrix. We conducted extensive experiments to validate our approach in six different datasets and compare its performance against hand-crafted methods.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

García-Pintos,

Aldana-Murillo,

Ovalle-Magallanes

et al. 2023

Eng

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“…For instance, geometric relations are needed for stitching together a series of images to generate a panorama image [2][3][4]. Other applications that require the estimation of geometric relations are associated to camera calibration, where each camera pose and focal length are computed with their associated correspondences [5][6][7][8]; to 3D reconstruction systems, where incremental structure from motion is applied using geometric entities given by the epipolar geometry [9,10]; to the removal of camera movements, when the motion of an object is studied in a video [11,12]; to the process of digitizing the appearance and geometry of objects in 3D, when textures are used [13]; to the control of robots, where homographies are used [14,15]; to topological mapping, where visual information is used [16]; to the reconstruction of 3D flame with color temperature, where they perform an epipolar plane matching and epipolar line equalization [17]; to the reality augmentation of buildings, where accurate homographies are needed [18], among others [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm

Lopez-Martinez

Cuevas

2020

Computers

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In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracking and more. The Random Sample Consensus (RANSAC) is the most popular heuristic technique to tackle this problem. However, RANSAC-like algorithms present a drawback regarding either the tuning of the number of samples and the threshold error or the computational burden. To relief this problem, we propose an estimator based on a metaheuristic, the Teaching–Learning-Based Optimization algorithm (TLBO) that is motivated by the teaching–learning process. We use the TLBO algorithm in the problem of computing multiple view relations given by the homography and the fundamental matrix. To improve the method, candidate models are better evaluated with a more precise objective function. To validate the efficacy of the proposed approach, several tests, and comparisons with two RANSAC-based algorithms and other metaheuristic-based estimators were executed.

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Visual Memory Construction for Autonomous Humanoid Robot Navigation

Cited by 2 publications

References 7 publications

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm

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