Panoramic Visual SLAM Technology for Spherical Images

Zhang, Yi; Huang, Fei

doi:10.3390/s21030705

Cited by 18 publications

(13 citation statements)

References 40 publications

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“…At this time, a negative number can be subtracted to achieve the purpose of reducing the error. erefore, after adjusting the parameters for many times, it is finally determined that the 1r value is 0.30, the batch is 8, and the epoch is 55, and the recognition accuracy is the highest and the most stable [23]. After modifying the parameters, the value of Topl of this method is 42.21%, and the value of Top5 is 88.77%.…”

Section: Comparison Of Action Recognition Algorithmsmentioning

confidence: 99%

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Yang

2022

International Transactions on Electrical Energy Systems

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At present, the application of information technology has realized the transformation of people’s production and lifestyle, and it has also promoted the transformation of the sports industry. At present, the application of informatization in the basketball field in the sports industry is increasing. By using the advanced methods and technologies of its information display, this article aims at realizing the optimization of the basketball recognition pattern in the mobile network communication environment to promote the ecological development of the sports industry. It proposes to use panoramic vision to guide the integration of computer network and basketball field, which is helpful to analyze and solve the objectivity problems of single action and system incompatibility in basketball recognition simulation. By referring to the principles and laws of panoramic vision, the identification module and simulation module of the basketball auxiliary training system are constructed and optimized, so as to realize the promotion of basketball and the ecologicalization of teaching. In the research on the basketball recognition simulation system, the three-dimensional skeleton key point coordinate data are input into the ST-GCN network for comparison and testing. Specifically, it includes 2D key point coordinate data and 3D key point coordinate data as input to conduct model research and testing in the self-made basketball dataset. The experimental results show that the recognition rates of 2D and 3D coordinates are 66.64% and 87.69%, respectively. Therefore, it is crucial to use the human panoramic vision algorithm to convert 2D coordinates to 3D coordinates.

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Section: Comparison Of Action Recognition Algorithmsmentioning

confidence: 99%

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Yang

2022

International Transactions on Electrical Energy Systems

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“…A panoramic spherical image is produced by combining multiple individual images to cover an extremely large angle of view, up to and including the full viewing sphere. To produce a panorama, individual images are algorithmically stitched together and mapped onto a virtual spherical surface, which is subsequently reprojected onto the 2D plane typically using the equirectangular projection (Zhang & Huang, 2021 ). Spherical panoramas are acquired either with a combination of two wide‐angle lenses via specially designed cameras systems, or by rotating a conventional camera about its horizontal and vertical axes whilst acquiring imagery (Fangi & Nardinocchi, 2013 ; Barbero‐García et al ., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

et al. 2022

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The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits.We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography.When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy.Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

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“…To address the above challenges, there are useful systems in the literature utilizing fisheye cameras for odometry or SLAM. [13], [14], [15], [16] and some feature-based SLAM systems [17], [18]. Other than multi-camera methods, the sensor fusions algorithm can provide better results in a complex environment.…”

Section: Introductionmentioning

confidence: 99%

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

Javed

Kim

2022

IEEE Access

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With the recent developments in computer vision, vision-based odometry plays an increasingly important role in the field of autonomous systems. However, using traditional visual odometry or simultaneous localization and mapping (vSLAM) only performs better in simple environments with obvious structural features. Visual odometry may easily fail in a complex environment due to the sparsity of stable features, sensor failure, and extreme weather conditions or sunlight problems. Monocular camera-based traditional algorithms are highly affected by these issues, leading to stability and reliability problems. Therefore, to deal with such issues, a vision-based Omni-directional odometry based on multi-camera collaboration is proposed. The development of feature-based omnidirectional odometry and feature prioritization to limit the computational complexity offered by multiple cameras is the major contribution. Firstly, the multicamera state perception model is developed based on the spherical camera, which guarantees an accurate transformation from the camera to the spherical coordinate system. The feature detection and tracking are performed in the images of the individual cameras in a parallel thread. The features tracking across the different cameras prevent failures, however, it also adds extra computational complexity for the pose estimation and optimization module. To budget the feature distribution, a feature prioritization algorithm is proposed to limit the number of features. Furthermore, the multi-view pose refinement module further reduces the complexity of the system. The feature prioritization helps to maintain the smaller set of tracked features with similar accuracy and less computational complexity. Finally, the pose is estimated in a spherical coordinate system by projecting all the successfully tracked key points to the sphere with the help of the omnidirectional perception model. To validate the proposed method, the data set is collected from the outdoor environment with ground truth provided by high-accuracy GPS. Detailed qualitative and quantitative evaluations are performed, which show that the proposed algorithm improves the position accuracy by about 40%-60% as compared with state-of-the-art methods with limited computation time.INDEX TERMS panoramic camera, multi-camera, visual odometry, vSLAM, GNSS/INS, Monocular camera.

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Panoramic Visual SLAM Technology for Spherical Images

Cited by 18 publications

References 40 publications

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

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