Multisensor Data Fusion

Durrant‐Whyte, Hugh; Henderson, Thomas C.

doi:10.1007/978-3-319-32552-1_35

Cited by 93 publications

(50 citation statements)

References 44 publications

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“…Kalman Filters. Kalman Filters are intimately related to recursive Bayesian filtering [34]. The popularity of KF was mostly thanks to its formulation, which allows many different sensor modalities to be arbitrarily modelled by the filter [46].…”

Section: Methods Of Fusionmentioning

confidence: 99%

“…It is also preferred for its ability to obtain the result in real time. The usual KF formulation follows a pattern of state-space modelling, and their subsequent prediction and update [34].…”

Section: Methods Of Fusionmentioning

confidence: 99%

“…Formally, the Kalman filter equation for state space input and output responses, in continuous time, are given by [34]:ẋ…”

Section: Methods Of Fusionmentioning

confidence: 99%

“…Additionally, F specifies system state matrix, B is the input matrix and H is the matrix specifying the observations. The usual KF approach has two phases, prediction and update, which we will omit in our formalisation and instead refer the reader to [34,46].…”

Section: Methods Of Fusionmentioning

confidence: 99%

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Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Section: Methods Of Fusionmentioning

confidence: 99%

Section: Methods Of Fusionmentioning

confidence: 99%

“…Formally, the Kalman filter equation for state space input and output responses, in continuous time, are given by [34]:ẋ…”

Section: Methods Of Fusionmentioning

confidence: 99%

Section: Methods Of Fusionmentioning

confidence: 99%

See 2 more Smart Citations

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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“…Noteworthy, Bayesian fusion is "cheap" in information form [18]. Extensive examples in SLAM [19] and multisensor data fusion [20] show that fusion in information form is more efficient than its dual as only requires an addition operation. However, it is often costly to recover the state estimate (mean and variance), unless the information matrix is sparse.…”

Section: Related Workmentioning

confidence: 99%

Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields

Sun

Vidal-Calleja

Miró

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Building large-scale 2.5D maps when spatial correlations are considered can be quite expensive, but there are clear advantages when fusing data. While optimal submapping strategies have been explored previously in covariance-form using Gaussian Process for large-scale mapping, this paper focuses on transferring such concepts into information form. By exploiting the conditional independence property of the Gaussian Markov Random Field (GMRF) models, we propose a submapping approach to build a nearly optimal global 2.5D map. In the proposed approach data is fused by first fitting a GMRF to one sensor dataset; then conditional independent submaps are inferred using this model and updated individually with new data arrives. Finally, the information is propagated from submap to submap to later recover the fully updated map. This is efficiently achieved by exploiting the inherent structure of the GMRF, fusion and propagation all in information form.The key contribution of this paper is the derivation of the algorithm to optimally propagate information through submaps by only updating the common parts between submaps. Our results show the proposed method reduces the computational complexity of the full mapping process while maintaining the accuracy. The performance is evaluated on synthetic data from the Canadian Digital Elevation Data.

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Background Appearance Modeling with Applications to Visual Object Detection in an Open‐Pit Mine

Bewley

Upcroft

2016

Journal of Field Robotics

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This paper addresses the problem of detecting people and vehicles on a surface mine by presenting an architecture that combines the complementary strengths of deep convolutional networks (DCN) with cluster‐based analysis. We highlight that using a DCN in a naïve black box approach results in a significantly high rate of errors due to the lack of mining‐specific training data and the unique landscape in a mine site. In this work, we propose a background model that exploits the abundance of background‐only images to discover the natural clusters in visual appearance using features extracted from the DCN. Both a simple nearest cluster‐based background model and an extended model with cosine features are investigated for their ability to identify and suppress potential false positives made by the DCN. Furthermore, localization of objects of interest is enabled through region proposals, which have been tuned to increase recall within the constraints of a computational budget. Finally, a soft fusion framework is presented to combine the estimates of both the DCN and background model to improve the accuracy of the detection. Our system is tested on over 11 km of real mine site data in both day and night conditions where we were able to detect both light and heavy vehicles along with mining personnel. We show that the introduction of our background model improves the detection performance. In particular, soft fusion of the background model and the DCN output produces a relative improvement in the F1 score of 46% and 28% compared to a baseline pretrained DCN and a DCN retrained with mining images, respectively.

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Multisensor Data Fusion

Cited by 93 publications

References 44 publications

Sensor Modalities and Fusion for Robust Indoor Localisation

Sensor Modalities and Fusion for Robust Indoor Localisation

Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields

Background Appearance Modeling with Applications to Visual Object Detection in an Open‐Pit Mine

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