Multi-camera localisation: a review

Raman, Rahul; Bakshi, Sambit; Pankaj, K.

doi:10.1504/ijmissp.2013.052876

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…Inicialmente, será realizada uma revisão bibliográfica de métodos de localização em sistemas multicâmeras [20] e métodos de detecção e reconhecimento de objetos [10]. Serão também implementados os gateways para o robô Lysa e as câmeras a serem utilizadas no sistema.…”

Section: Proposta De Trabalhounclassified

MobiLysa - Sistema de localização e controle do cão-guia robô Lysa para ambientes internos baseado em visão computacional

Cotta

Machado

Vassallo

et al. 2019

Anais Estendidos Do Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia)

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Lysa is a guide-robot whose goal is to assist people with visual impairment, improving their autonomy and mobility. Lysa is able to detect potholes, ground obstacles and risks of collision with high objects, warning the user of such dangers by voice. However Lysa is still unable to guide a person to a place within an environment. Therefore, the aim of this project will be to develop MobiLysa: a service to perform localization, navigation and control of the robot Lysa so it can guide a person to a desired location inside a building. That will be done using an Intelligent Space based on computer vision. The user will be able to indicate the desired destination, either by voice or other form of interaction, and be guided to the place independently and safely.

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Section: Proposta De Trabalhounclassified

MobiLysa - Sistema de localização e controle do cão-guia robô Lysa para ambientes internos baseado em visão computacional

Cotta

Machado

Vassallo

et al. 2019

Anais Estendidos Do Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia)

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“…No matter which method is used, estimating the positions and orientations of cameras from different views is the pre-requisite for the reconstruction task. This process is known as camera localization [3].…”

Section: Introductionmentioning

confidence: 99%

A simple method to achieve full-field and real-scale reconstruction using a movable stereo rig

Zhao

Song

et al. 2018

Meas. Sci. Technol.

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This paper introduces a simple method to achieve full-field and real-scale reconstruction using a movable binocular vision system (MBVS). The MBVS is composed of two cameras, one is called the tracking camera, and the other is called the working camera. The tracking camera is used for tracking the positions of the MBVS and the working camera is used for the 3D reconstruction task. The MBVS has several advantages compared with a single moving camera or multi-camera networks. Firstly, the MBVS could recover the real-scale-depth-information from the captured image sequences without using auxiliary objects whose geometry or motion should be precisely known. Secondly, the removability of the system could guarantee appropriate baselines to supply more robust point correspondences. Additionally, using one camera could avoid the drawback which exists in multi-camera networks, that the variability of a cameras’ parameters and performance could significantly affect the accuracy and robustness of the feature extraction and stereo matching methods. The proposed framework consists of local reconstruction and initial pose estimation of the MBVS based on transferable features, followed by overall optimization and accurate integration of multi-view 3D reconstruction data. The whole process requires no information other than the input images. The framework has been verified with real data, and very good results have been obtained.

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A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

et al. 2023

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Camera localization involves the estimation of the camera pose of an image from a random scene. We used a single image or sequence of images or videos as the input. The output depends on the representation of the scene and method used. Several computer vision applications, such as robot navigation and safety inspection, can benefit from camera localization. Camera localization is used to determine the position of an object on the camera in an image containing multiple images in a sequence. Structure-based localization techniques have achieved considerable success owing to a combination of image matching and coordinate regression. Absolute and relative pose regression techniques can provide end-to-end learning; however, they exhibit poor accuracies. Despite the rapid growth in computer vision, there has been no thorough review of the categorization, evaluation, and synthesis of structures and regression-based techniques. Input format and loss strategies for recurrent neural networks (RNN) have not been adequately described in the literature. The main topic is indoor camera pose regression, which is a part of the camera localization techniques. First, we discuss certain application areas for camera localization. We then discuss different camera localization techniques, such as feature and structure-based, absolute and relative pose regression techniques, and simultaneous localization and mapping (SLAM). We evaluated the frequently used datasets and qualitatively compared the absolute and relative camera pose estimation approaches. Finally, we discuss potential directions for future research, such as optimizing the computational cost of the features and evaluating the end-to-end characteristics of multiple cameras.INDEX TERMS Camera pose regression, absolute pose regression, indoor positioning, camera localization, robot navigation, SLAM.

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Multi-camera localisation: a review

Cited by 3 publications

References 65 publications

MobiLysa - Sistema de localização e controle do cão-guia robô Lysa para ambientes internos baseado em visão computacional

MobiLysa - Sistema de localização e controle do cão-guia robô Lysa para ambientes internos baseado em visão computacional

A simple method to achieve full-field and real-scale reconstruction using a movable stereo rig

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

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