On 3d Model Construction By Fusing Heterogeneous Sensor Data

Wang, Y.F.; Yang, Zaiming; Lee, Jeng-Feng

doi:10.1109/iros.1992.594523

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…With KFs, sequentially-obtained data points or sets can be dynamically integrated without requiring all previous data kept in storage. Successful KF applications in surface reconstruction can be found elsewhere [20][21][22]. In this section, we show that WLS fusion and KFs lead to the same fusion results.…”

Section: The Relationship With Kalman Filtersmentioning

confidence: 60%

Study of weighted fusion methods for the measurement of surface geometry

Wang

Pagani

Leach

et al. 2017

Precision Engineering

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights:  Four weighted fusion methods for surface measurement were classified and analysed.  The uncertainty propagation and the relationship with Kalman filter were analysed.  Advanced models compatible with different fusion methods were described.  Experiments verified the effectiveness of weighted fusion on accuracy improvement.  Weighted fusion complements residual approximation fusion in many fusion scenarios. AbstractFour types of weighted fusion methods, including pixel-level, least-squares, parametrical and non-parametrical, have been classified and theoretically analysed in this study. In particular, the uncertainty propagation of the weighted least-squares fusion was analysed and its relation to the Kalman filter was studied. In cooperation with different fitting models, these four weighted fusion methods can be applied to a range of measurement challenges.The experimental results of this study show that the four weighted fusion methods compose a computationally efficient and reliable system for multi-sensor measurement problems, especially for freeform surface measurement. A comparison of weighted fusion with residual approximation-based fusion has also been conducted by providing the input datasets with different noise levels and sample sizes. The results demonstrated that weighted fusion and residual approximation-based fusion are complementary approaches applicable to most fusion scenarios.

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Section: The Relationship With Kalman Filtersmentioning

confidence: 60%

Study of weighted fusion methods for the measurement of surface geometry

Wang

Pagani

Leach

et al. 2017

Precision Engineering

View full text Add to dashboard Cite

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3D scene modelling and curve-based localization in natural environments

Devy

Parra²

Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)

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Navigation for Performing Visual Surveys of Non-Planar Surfaces

Murthy

Rock

2009

AIAA Guidance, Navigation, and Control Conference

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This paper addresses the problem of navigating a robotic vehicle over non-planar surfaces for the purpose of performing visual surveys. The key issue lies in generating a spatiallysmooth camera orientation command that will point the camera toward the surface during surface traversal. The proposed solution filters high spatial frequency local surface measurements using a low spatial frequency spline-based model of the surface. The filtering occurs by updating the spline surface model with local measurements using a linear Kalman filter. Subsequently, the spline point projection algorithm generates the camera orientation command from the updated spline surface. The results show that when the spline-based surface model is used to smooth the high frequency measurements, the resulting camera orientation command resembles a spatially-smoothed version of the orientation command produced by local surface orientation measurements. Though the results of this paper focus on underwater visual survey applications, the proposed solution could be applied to any visual survey of a high spatial frequency surface, such as spacecraft inspection.

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On 3d Model Construction By Fusing Heterogeneous Sensor Data

Cited by 3 publications

References 6 publications

Study of weighted fusion methods for the measurement of surface geometry

Study of weighted fusion methods for the measurement of surface geometry

3D scene modelling and curve-based localization in natural environments

Navigation for Performing Visual Surveys of Non-Planar Surfaces

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