This paper presents a prototype two-in-one, dense-plus-sparse depth module designed for Mixed Reality headsets that uses a novel approach to sparse depth imaging by combining indirect Time-of-Flight (iToF) and triangu...
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ISBN:
(纸本)9781510686069;9781510686076
This paper presents a prototype two-in-one, dense-plus-sparse depth module designed for Mixed Reality headsets that uses a novel approach to sparse depth imaging by combining indirect Time-of-Flight (iToF) and triangulation modalities to provide improved range and accuracy. The module uses an ADI ADSD3030 VGA iToF sensor and reduces module size from 62 x 24 x 13 mm in HoloLens 2 to 33.5 x 15 x 7.2 mm. Sparse frames have temporal noise of <1% of distance at 3% reflectivity, 3 klx sunlight equivalent spectrum and can image 95% reflectivity targets out to the ambiguity distance of 21 m while consuming 14.7 mJ per-frame. New low-compute algorithms that combine iToF and triangulation are presented and are demonstrated to mitigate systematic errors such as multipath and subsurface diffusion as well as extend dynamic range under full-sunlight and high-signal conditions. sensorapplications include scene understanding, object capture and hand-tracking.
The proceedings contains 31 papers from the conference on sensorfusion: architectures, algorithms, and applications vi. Topics discussed include: adaptive sequential Bayesian classification using Page's test;onto...
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The proceedings contains 31 papers from the conference on sensorfusion: architectures, algorithms, and applications vi. Topics discussed include: adaptive sequential Bayesian classification using Page's test;ontology-based multiagent approach to data fusion;unified approach to the fusion of imperfect data;fusion approach to stereo vision uncertainity;robust fusion with reliabilities weights;distributed air-to-ground targeting;and fuser design for thick film pH sensor electrodes using emperical data.
作者:
Braun, JJMIT
Lincoln Lab Lexington MA 02420 USA
This paper presents an approach to multisensor data fusion based on the use of Support Vector Machines (SVM). The approach is investigated using simulated generic sensor data, representative of data imperfections that...
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ISBN:
(纸本)0819444812
This paper presents an approach to multisensor data fusion based on the use of Support Vector Machines (SVM). The approach is investigated using simulated generic sensor data, representative of data imperfections that may be encountered in multisensorfusionapplications. In particular the issue of data incompleteness is addressed and a method exploiting vicinity of training points is proposed for incompleteness correction. The paper also investigates applicability of vicinal kernels in SVM-based sensor data fusion.
In this paper we describe the nature of the problem of surveillance of airport surface movement. We describe the characteristics, performance, and unique problems of various airport sensors available, and the need to ...
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ISBN:
(纸本)0819444812
In this paper we describe the nature of the problem of surveillance of airport surface movement. We describe the characteristics, performance, and unique problems of various airport sensors available, and the need to develop a fusion system to provide an integrated surveillance picture. Parallel sensorfusion developments are described in terms of their applicability to the sensorfusion task in surface surveillance. Paradigms for sensorfusion, including alternative architectures, algorithms, and performance metrics will be described. Finally we describe system implementation and quantitative performance of sensorfusion applied to the surface surveillance problem at demonstrations in Atlanta Hartsfield International Airport (1998, ATL), Dallas Fort Worth International Airport (1999, 2000, DFW), and in-process and planned future developments in sensorfusion.
Data fusion architecture can be categorized into data-level fusion, feature-level fusion and decision-level fusion by its characteristics. In this paper, we provide a new target identification fusion technology in whi...
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ISBN:
(纸本)0819444812
Data fusion architecture can be categorized into data-level fusion, feature-level fusion and decision-level fusion by its characteristics. In this paper, we provide a new target identification fusion technology in which we adopt not only feature-level fusion approach but also decision-level fusion approach in order to consider even sensors' uncertain reports and improve fusion performance. In feature-level fusion stage, we applied fuzzy set theory and Bayesian theory based on the sensor data, such as sensor parameter and detected target information. In decision-level fusion stage, we applied advanced Bayesian theory to decide final target identification. Experimental results with various kinds of sensor data have verified the robustness of our algorithms comparing with conventional feature-level, decision-level fusion algorithms.
In this paper, a series of knowledge fusion operators are motivated and analyzed. They are defined in a semantic way, although syntactical facets of knowledge are taken into account. More precisely, they rely on a ran...
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ISBN:
(纸本)0819444812
In this paper, a series of knowledge fusion operators are motivated and analyzed. They are defined in a semantic way, although syntactical facets of knowledge are taken into account. More precisely, they rely on a rank-ordering of interpretations that is based on the number of formulas that the interpretations falsify. It is briefly discussed how these operators could be refined, by taking into account various distribution policies of the falsified information among the knowledge sources, syntactical properties of formulas to be fused and forms of integrity constraints preference among literals.
Modern technology provides a great amount of information. In computer monitoring systems or computer control systems, especially real-time expert systems, in order to have the situation in hand, we need one or two par...
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ISBN:
(纸本)0819444812
Modern technology provides a great amount of information. In computer monitoring systems or computer control systems, especially real-time expert systems, in order to have the situation in hand, we need one or two parameters to express the quality and/or security of the whole system. This paper presents a principle for synthesizing measurements of multiple system parameters into a single parameter. This principle has been successfully applied in the monitoring of an ultra-energy efficient house in Canada and other applications.
This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter theta. A multisensor c...
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ISBN:
(纸本)0819444812
This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter theta. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.
In a multi-node distributed decision system under some conditions there are few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. However if we t...
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ISBN:
(纸本)0819444812
In a multi-node distributed decision system under some conditions there are few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. However if we treat this distributed system as a multi-agent system, and each node acts as an agent, it has some other node's historical experiences or knowledge for resolving problems and stored in additional case bases, so it can uses case based reasoning (CBR) and transposition reasoning to obtain the possible viewpoints or decisions of other nodes and then makes information fusion by itself This approach may reduce the subjectivism which is the weakness of pure transposition reasoning.
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