&lt;title&gt;Multisensor data fusion of points, line segments, and surface segments in 3D space&lt;/title&gt;

Pabst, Joost van Lawick van; Krekel, P. F.

doi:10.1117/12.150254

Cited by 3 publications

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“…Examples of navigation include global positioning systems [1] and navigation of road vehicles [18]. Other applications are in data association and fusion [22].…”

Section: Introductionmentioning

confidence: 99%

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

Bolić

Athalye

Hong

et al. 2009

J Sign Process Syst

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In this paper, we analyze algorithmic and architectural characteristics of a class of particle filters known as Gaussian Particle Filters (GPFs). GPFs approximate the posterior density of the unknowns with a Gaussian distribution which limits the scope of their applications in comparison with the universally applied sample-importance resampling filters (SIRFs) but allows for their implementation without the classical resampling procedure. Since there is no need for resampling, we propose a modified GPF algorithm that is suitable for parallel hardware realization. Based on the new algorithm, we propose an efficient parallel and pipelined architecture for GPF that is superior to similar architectures for SIRF in the sense that it requires no memories for storing particles and it has very low amount of data exchange through the communication network. We analyze the GPF on the bearings-only tracking problem and the results are compared with results obtained by SIRF in terms of computational complexity, potential throughput, and hardware energy. We consider implementation on FPGAs and we perform detailed comparison of the GPF and SIRF algorithms implemented in different ways on this platform. GPFs that are implemented in parallel pipelined fashion on FPGAs can support higher sampling rates than SIRFs and as such they might be a more suitable candidate for real-time applications.

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“…Examples of navigation include global positioning systems [1] and navigation of road vehicles [18]. Other applications are in data association and fusion [22].…”

Section: Introductionmentioning

confidence: 99%

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

Bolić

Athalye

Hong

et al. 2009

J Sign Process Syst

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Kalman Filter

Welch

2014

Computer Vision

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A Dynamic Sensing-and-Modeling Approach to Three-Dimensional Point- and Area-Sensor Integration

Huang

Qian

2006

Journal of Manufacturing Science and Engineering

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The recent advancement of 3D non-contact laser scanners enables fast measurement of parts by generating a huge amount of coordinate data for a large surface area in a short time. In contrast, traditional tactile probes in the coordinate measurement machines can generate more accurate coordinate data points at a much slower pace. Therefore, the combination of laser scanners and touch probes can potentially lead to more accurate, faster, and denser measurements. In this paper, we develop a dynamic sensing-and-modeling approach for integrating a tactile point sensor and an area laser scanner to improve the measurement speed and quality. A part is first laser scanned to capture its overall shape. It is then probed via a tactile sensor where the probing positions are dynamically determined to reduce the measurement uncertainty based on a novel next-best-point formulation. Technically, we use the Kalman filter to fuse laser-scanned point cloud and tactile points and to incrementally update the surface model based on the dynamically probed points. We solve the next-best-point problem by transforming the B-spline surface’s uncertainty distribution into a higher dimensional uncertainty surface so that the convex hull property of the B-spline surface can be utilized to dramatically reduce the search speed and to guarantee the optimality of the resulting point. Three examples in this paper demonstrate that the dynamic sensing-and-modeling effectively integrates the area laser scanner and the point touch probe and leads to a significant amount of measurement time saving (at least several times faster in all three cases). This dynamic approach’s further benefits include reducing surface uncertainty due to the maximum uncertainty control through the next-best-point sensing and improving surface accuracy in surface reconstruction through the use of Kalman filter to account various sensor noise.

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<title>Multisensor data fusion of points, line segments, and surface segments in 3D space</title>

Cited by 3 publications

References 0 publications

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

Kalman Filter

A Dynamic Sensing-and-Modeling Approach to Three-Dimensional Point- and Area-Sensor Integration

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