An iterative scheme for feature based positioning using a weighted dissimilarity measure

We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the reference fingerprint map (RFM). Spatial filtering and kernel smoothing of the kinematically collected raw data allow efficiently estimating the standard deviations during RFM generation. In the positioning stage, the weights control the contribution of each feature to the dissimilarity measure, which in turn quantifies the difference between the set of online measured features and the fingerprints stored in the RFM. Features with little variability contribute more to the estimated position than features with high variability. Iterations are necessary because the variability depends on the location, and the location is initially unknown when estimating the position. Using real WiFi signal strength data from extended test measurements with ground truth in an office building, we show that the standard deviations of these features vary considerably within the region of interest and are neither simple functions of the signal strength nor of the distances from the corresponding access points. This is the motivation to include the empirical standard deviations in the RFM. We then analyze the deviations of the estimated positions with and without the location-dependent weighting. In the present example the maximum radial positioning error from ground truth are reduced by 40% comparing to kNN without the weighted dissimilarity measure.Comment: 18 pages, 9 figures, and 1 tabl

Modified Jaccard Index Analysis and Adaptive Feature Selection for Location Fingerprinting with Limited Computational Complexity

We propose an approach for fingerprinting-based positioning which reduces the data requirements and computational complexity of the online positioning stage. It is based on a segmentation of the entire region of interest into subregions, identification of candidate subregions during the online-stage, and position estimation using a preselected subset of relevant features. The subregion selection uses a modified Jaccard index which quantifies the similarity between the features observed by the user and those available within the reference fingerprint map. The adaptive feature selection is achieved using an adaptive forward-backward greedy search which determines a subset of features for each subregion, relevant with respect to a given fingerprinting-based positioning method. In an empirical study using signals of opportunity for fingerprinting the proposed subregion and feature selection reduce the processing time during the online-stage by a factor of about 10 while the positioning accuracy does not deteriorate significantly. In fact, in one of the two study cases the 90th percentile of the circular error increased by 7.5% while in the other study case we even found a reduction of the corresponding circular error by 30%.Comment: 15 pagers, 10 figures, 10 tables, revised version for publishing to TLBS. arXiv admin note: text overlap with arXiv:1711.0781

Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS

Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS and achieving the position estimation by reusing the available WLAN and mobile devices, and capable of providing absolute position estimation. For fingerprinting based positioning (FbP), a model is created to provide reference values of observable features (e.g., signal strength from access point (AP)) as a function of location during offline stage. One widely applied method to build a complete and an accurate reference database (i.e. radio map (RM)) for FWIPS is carrying out a site survey throughout the region of interest (RoI). Along the site survey, the readings of received signal strength (RSS) from all visible APs at each reference point (RP) are collected. This site survey, however, is time-consuming and labor-intensive, especially in the case that the RoI is large (e.g., an airport or a big mall). This bottleneck hinders the wide commercial applications of FWIPS (e.g., proximity promotions in a shopping center). To diminish the cost of site survey, we propose a probabilistic model, which combines fingerprinting based positioning (FbP) and RM generation based on stochastic variational Bayesian inference (SVBI). This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation. These benefits make it outperforms the previous proposed approaches.Comment: 10 pages, 16 figures, and 2 tables. A paper under review of IPIN 201

Feature-wise change detection and robust indoor positioning using RANSAC-like approach

Fingerprinting-based positioning, one of the promising indoor positioning solutions, has been broadly explored owing to the pervasiveness of sensor-rich mobile devices, the prosperity of opportunistically measurable location-relevant signals and the progress of data-driven algorithms. One critical challenge is to controland improve the quality of the reference fingerprint map (RFM), which is built at the offline stage and applied for online positioning. The key concept concerningthe quality control of the RFM is updating the RFM according to the newly measured data. Though varies methods have been proposed for adapting the RFM, they approach the problem by introducing extra-positioning schemes (e.g. PDR orUGV) and directly adjust the RFM without distinguishing whether critical changes have occurred. This paper aims at proposing an extra-positioning-free solution by making full use of the redundancy of measurable features. Loosely inspired by random sampling consensus (RANSAC), arbitrarily sampled subset of features from the online measurement are used for generating multi-resamples, which areused for estimating the intermediate locations. In the way of resampling, it can mitigate the impact of the changed features on positioning and enables to retrieve accurate location estimation. The users location is robustly computed by identifying the candidate locations from these intermediate ones using modified Jaccardindex (MJI) and the feature-wise change belief is calculated according to the world model of the RFM and the estimated variability of features. In order to validate our proposed approach, two levels of experimental analysis have been carried out. On the simulated dataset, the average change detection accuracy is about 90%. Meanwhile, the improvement of positioning accuracy within 2 m is about 20% by dropping out the features that are detected as changed when performing positioning comparing to that of using all measured features for location estimation. On the long-term collected dataset, the average change detection accuracy is about 85%.Comment: 36 pages, 20 figures, 2 table

WiFi based trajectory alignment, calibration and easy site survey using smart phones and foot-mounted IMUs

Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed to align and calibrate the trajectories estimated from foot mounted inertial measurement units (IMUs). A crowd-sourced radio map (RM) can be built subsequently and can be used for fingerprinting based Wifi indoor positioning (FWIP). The foundation of the proposed approach is graph based simultaneously localization and mapping (SLAM). The nodes in the graph denote users poses and the edges denote the pairwise constrains between the nodes. The constrains are derived from: (1) inertial estimated trajectories; (2) vicinity in the RSS space. With these constrains, an error functions is defined. By minimizing the error function, the graph is optimized and the aligned/calibrated trajectories along with the RM are acquired. The experimental results have corroborated the effectiveness of the approach for trajectory alignment, calibration as well as RM construction.Comment: 9 figures, 6 pages, paper under review of IPIN 201

Application of backpropagation neural networks to both stages of fingerprinting based WIPS

We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinate vectors and associated WLAN/WiFi signal strengths of the available access points, a BPNN can be trained to output the expected signal strengths for any input position within the region of interest (BPNN-RM). This can be used to provide a continuous representation of the radio map and to filter, densify or decimate a discrete radio map. Correspondingly, the TRM can also be used to train another BPNN to output the expected position within the region of interest for any input vector of recorded signal strengths and thus carry out localization (BPNN-LA).Key aspects of the design of such artificial neural networks for a specific application are the selection of design parameters like the number of hidden layers and nodes within the network, and the training procedure. Summarizing extensive numerical simulations, based on real measurements in a testbed, we analyze the impact of these design choices on the performance of the BPNN and compare the results in particular to those obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest neighbors approaches to FWIPS.Comment: 11 pages, 11 figures, published in proceedings UPINLBS 201

Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning

Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper, we propose an adaptive neighboring selection algorithm, which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry, Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results. By quantitative analysis, the embedding quality measured by residual variance is increased 45.45% after using the proposed algorithm in LLE.Comment: 3 figures, from Journal of Harbin Institute of Technolog

Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization

With the recent development in mobile computing devices and as the ubiquitous deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN based indoor localization systems(WILSs) are of mounting concentration and are becoming more and more prevalent for they do not require additional infrastructure. As to the localization methods in WILSs, for the approaches used to localization in satellite based global position systems are difficult to achieve in indoor environments, fingerprint based localization algorithms(FLAs) are predominant in the RSS based schemes. However, the performance of FLAs has close relationship with the number of APs and the number of reference points(RPs) in WILSs, especially as the redundant deployment of APs and RPs in the system. There are two fatal problems, curse of dimensionality (CoD) and asymmetric matching(AM), caused by increasing number of APs and breaking down APs during online stage. In this paper, a semi-supervised RSS dimensionality reduction algorithm is proposed to solve these two dilemmas at the same time and there are numerous analyses about the theoretical realization of the proposed method. Another significant innovation of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm to improve the localization accuracy of indoor localization.Comment: 14 figures. Institute of Navigation (ION GNSS+ 2014), 27th International Technical Meeting of The Satellite Division Conference o

Mining geometric constraints from crowd-sourced radio signals and its application to indoor positioning

Crowd-sourcing has become a promising way to build} a feature-based indoor positioning system that has lower labour and time costs. It can make full use of the widely deployed infrastructure as well as built-in sensors on mobile devices. One of the key challenges is to generate the reference feature map (RFM), a database used for localization, by {aligning crowd-sourced {trajectories according to associations embodied in the data. In order to facilitate the data fusion using crowd-sourced inertial sensors and radio signals, this paper proposes an approach to adaptively mining geometric information. This is the essential for generating spatial associations between trajectories when employing graph-based optimization methods. The core idea is to estimate the functional relationship to map the similarity/dissimilarity between radio signals to the physical space based on the relative positions obtained from inertial sensors and their associated radio signals. Namely, it is adaptable to different modalities of data and can be implemented in a self-supervised way. We verify the generality of the proposed approach through comprehensive experimental analysis: i) qualitatively comparing the estimation of geometric mapping models and the alignment of crowd-sourced trajectories; ii) quantitatively evaluating the positioning performance. The 68\% of the positioning error is less than 4.7 $\mathrm{m}$ using crowd-sourced RFM, which is on a par with manually collected RFM, in a multi-storey shopping mall, which covers more than 10, 000 $\mathrm{m}^2$.Comment: 20 pages, 11 figures, accepted to publish on IEEE Acces

The Perfect Match: 3D Point Cloud Matching with Smoothed Densities

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep learning architecture and fully convolutional layers using a voxelized smoothed density value (SDV) representation. The latter is computed per interest point and aligned to the local reference frame (LRF) to achieve rotation invariance. Our compact, learned, rotation invariant 3D point cloud descriptor achieves 94.9% average recall on the 3DMatch benchmark data set, outperforming the state-of-the-art by more than 20 percent points with only 32 output dimensions. This very low output dimension allows for near realtime correspondence search with 0.1 ms per feature point on a standard PC. Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers. We show that 3DSmoothNet trained only on RGB-D indoor scenes of buildings achieves 79.0% average recall on laser scans of outdoor vegetation, more than double the performance of our closest, learning-based competitors. Code, data and pre-trained models are available online at https://github.com/zgojcic/3DSmoothNet.Comment: CVPR 201