A Visual and VAE Based Hierarchical Indoor Localization Method

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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Global whole-rock geochemical database compilation

10.5194/essd-2019-50 ◽

2019 ◽

Cited By ~ 2

Author(s):

Matthew Gard ◽

Derrick Hasterok ◽

Jacqueline Halpin

Keyword(s):

Database Management ◽

Temporal Trends ◽

Physical Property ◽

Database Management System ◽

Geochemical Data ◽

Data Sets ◽

Unique Contribution ◽

Data Set ◽

Wide Range ◽

Geochemical Indices

Abstract. Dissemination and collation of geochemical data are critical to promote rapid, creative and accurate research and place new results in an appropriate global context. To this end, we have assembled a global whole-rock geochemical database, with other associated sample information and properties, sourced from various existing databases and supplemented with numerous individual publications and corrections. Currently the database stands at 1,023,490 samples with varying amounts of associated information including major and trace element concentrations, isotopic ratios, and location data. The distribution both spatially and temporally is quite heterogeneous, however temporal distributions are enhanced over some previous database compilations, particularly in terms of ages older than ~ 1000 Ma. Also included are a wide range of computed geochemical indices, physical property estimates and naming schema on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated by sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data is published in .csv format for the purposes of simple distribution but exists in a format acceptable for database management systems (e.g. SQL). One can either manipulate this data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload to a relational database management system for easy querying and management of the data as unique keys already exist. This data set will continue to grow, and we encourage readers to contact us or other database compilations contained within about any data that is yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592823 (Gard et al., 2019).

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Panoramic stitching of heterogeneous single-cell transcriptomic data

10.1101/371179 ◽

2018 ◽

Cited By ~ 17

Author(s):

Brian Hie ◽

Bryan Bryson ◽

Bonnie Berger

Keyword(s):

Single Cell ◽

Cell Types ◽

Data Sets ◽

Cell Type ◽

Data Set ◽

Wide Range ◽

Data Set Integration ◽

Biological Patterns ◽

Insight Into ◽

Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

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Characterising RDF data sets

Journal of Information Science ◽

10.1177/0165551516677945 ◽

2017 ◽

Vol 44 (2) ◽

pp. 203-229 ◽

Cited By ~ 6

Author(s):

Javier D Fernández ◽

Miguel A Martínez-Prieto ◽

Pablo de la Fuente Redondo ◽

Claudio Gutiérrez

Keyword(s):

Data Structures ◽

Large Scale ◽

Open Data ◽

Structural Features ◽

Data Sets ◽

Data Set ◽

Wide Range ◽

Rdf Data ◽

Description Framework ◽

Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

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Improved SfM-Based Indoor Localization with Occlusion Removal

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2018070102 ◽

2018 ◽

Vol 10 (3) ◽

pp. 24-40

Author(s):

Yushi Li ◽

George Baciu ◽

Yu Han ◽

Chenhui Li

Keyword(s):

Indoor Localization ◽

Sparse Matrix ◽

Matrix Decomposition ◽

Low Rank ◽

Data Sets ◽

Indoor Environments ◽

Decomposition Approach ◽

Localization Accuracy ◽

Occlusion Removal ◽

Accuracy Increase

This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.

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A New Approach for Supervised Dimensionality Reduction

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2018100102 ◽

2018 ◽

Vol 14 (4) ◽

pp. 20-37 ◽

Cited By ~ 1

Author(s):

Yinglei Song ◽

Yongzhong Li ◽

Junfeng Qu

Keyword(s):

Eigenvalue Problem ◽

Dimensionality Reduction ◽

Image Databases ◽

Data Sets ◽

Data Set ◽

New Approach ◽

Local Structures ◽

Benchmark Data ◽

Global And Local

This article develops a new approach for supervised dimensionality reduction. This approach considers both global and local structures of a labelled data set and maximizes a new objective that includes the effects from both of them. The objective can be approximately optimized by solving an eigenvalue problem. The approach is evaluated based on a few benchmark data sets and image databases. Its performance is also compared with a few other existing approaches for dimensionality reduction. Testing results show that, on average, this new approach can achieve more accurate results for dimensionality reduction than existing approaches.

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A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering

10.24271/psr.18 ◽

2021 ◽

Vol 3 (1) ◽

pp. 1-7

Author(s):

Yadgar Sirwan Abdulrahman

Keyword(s):

Clustering Algorithm ◽

Similarity Criterion ◽

Real Data ◽

Well Being ◽

Classical Solutions ◽

Data Sets ◽

Data Set ◽

New Approach ◽

Fuzzy C Means Clustering ◽

Global And Local

Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features

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Clusterdv, a simple density-based clustering method that is robust, general and automatic

10.1101/224840 ◽

2017 ◽

Author(s):

João C. Marques ◽

Michael B. Orger

Keyword(s):

Clustering Algorithm ◽

Underlying Structure ◽

Data Sets ◽

Natural Phenomena ◽

Cluster Number ◽

Data Set ◽

Density Peaks ◽

Wide Range ◽

Cluster Shape ◽

Fully Automatic

AbstractHow to partition a data set into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data varies widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be broadly applied. One recent method, clusterdp, based on search of density peaks, can be applied successfully to cluster many kinds of data, but it is not fully automatic, and fails on some simple data distributions. We propose an alternative approach, clusterdv, which estimates density dips between points, and allows robust determination of cluster number and distribution across a wide range of data, without any manual parameter adjustment. We show that this method is able to solve a range of synthetic and experimental data sets, where the underlying structure is known, and identifies consistent and meaningful clusters in new behavioral data.Author summarIt is common that natural phenomena produce groupings, or clusters, in data, that can reveal the underlying processes. However, the form of these clusters can vary arbitrarily, making it challenging to find a single algorithm that identifies their structure correctly, without prior knowledge of the number of groupings or their distribution. We describe a simple clustering algorithm that is fully automatic and is able to correctly identify the number and shape of groupings in data of many types. We expect this algorithm to be useful in finding unknown natural phenomena present in data from a wide range of scientific fields.

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Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

Sensors ◽

10.3390/s18082692 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2692 ◽

Cited By ~ 17

Author(s):

Yujin Chen ◽

Ruizhi Chen ◽

Mengyun Liu ◽

Aoran Xiao ◽

Dewen Wu ◽

...

Keyword(s):

Image Retrieval ◽

Pose Estimation ◽

Indoor Localization ◽

Location Based Services ◽

Visual Localization ◽

Indoor Environments ◽

Deep Convolutional Neural Networks ◽

Accurate Localization ◽

Visual Odometer ◽

Poisson errors and adaptive rebinning in X-ray powder diffraction data

Powder Diffraction ◽

10.1017/s0885715618000726 ◽

2018 ◽

Vol 33 (4) ◽

pp. 266-269 ◽

Cited By ~ 1

Author(s):

Marcus H. Mendenhall

Keyword(s):

Powder Diffraction ◽

Diffraction Data ◽

Data Sets ◽

Powder Diffraction Data ◽

Data Set ◽

X Ray ◽

Short Summary ◽

Correct Assignment ◽

Wide Range ◽

Diffraction Patterns

This work provides a short summary of techniques for formally-correct handling of statistical uncertainties in Poisson-statistics dominated data, with emphasis on X-ray powder diffraction patterns. Correct assignment of uncertainties for low counts is documented. Further, we describe a technique for adaptively rebinning such data sets to provide more uniform statistics across a pattern with a wide range of count rates, from a few (or no) counts in a background bin to on-peak regions with many counts. This permits better plotting of data and analysis of a smaller number of points in a fitting package, without significant degradation of the information content of the data set. Examples of the effect of this on a diffraction data set are given.

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Quantitative Electron Microprobe Analysis Of Semiconductor Materials: An Evaluation Of Accuracy

Microscopy and Microanalysis ◽

10.1017/s1431927600013696 ◽

1999 ◽

Vol 5 (S2) ◽

pp. 74-75

Author(s):

P.K. Carpenter

Keyword(s):

Ternary Systems ◽

Data Sets ◽

Silicate Mineral ◽

Data Set ◽

X Ray ◽

Quantitative Microanalysis ◽

Wide Range ◽

Analytical Accuracy ◽

Unknown Composition ◽

Counting Statistics

Both precision and accuracy are central to quantitative microanalysis. While precision may be evaluated from x-ray counting statistics and replicate measurement, the determination of analytical accuracy requires well characterized standards of which there are few that span a wide range of compositions in binary and ternary systems. The accuracy of silicate mineral analysis has been previously studied via measurement of α factors at multiple accelerating potential and the subsequent evaluation of correction algorithms and mass absorption coefficient (mac) data sets. This approach has been extended in this study to the In2O3-Ga2O3 and HgTe-CdTe systems.Single crystals of ln2O3, Ga2O3, and an InGa-oxide of unknown composition were used to evaluate accuracy in the In2O3-Ga2O3 binary, using the GaKα, GaLα, and InLα x-ray lines, with WDS measurements performed at 15, 20, and 25KV relative to the ln2O3 and Ga2O3 standards (see Table I). The Ga Kα line exhibits minimal absorption, has no fluorescence correction in this system and is not critically dependent on the correction algorithm or mac data set used.

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