6,437 research outputs found
Intrinsic Isometric Manifold Learning with Application to Localization
Data living on manifolds commonly appear in many applications. Often this
results from an inherently latent low-dimensional system being observed through
higher dimensional measurements. We show that under certain conditions, it is
possible to construct an intrinsic and isometric data representation, which
respects an underlying latent intrinsic geometry. Namely, we view the observed
data only as a proxy and learn the structure of a latent unobserved intrinsic
manifold, whereas common practice is to learn the manifold of the observed
data. For this purpose, we build a new metric and propose a method for its
robust estimation by assuming mild statistical priors and by using artificial
neural networks as a mechanism for metric regularization and parametrization.
We show successful application to unsupervised indoor localization in ad-hoc
sensor networks. Specifically, we show that our proposed method facilitates
accurate localization of a moving agent from imaging data it collects.
Importantly, our method is applied in the same way to two different imaging
modalities, thereby demonstrating its intrinsic and modality-invariant
capabilities
Machine Learning in Appearance-based Robot Self-localization
An appearance-based robot self-localization problem is considered in the
machine learning framework. The appearance space is composed of all possible
images, which can be captured by a robot's visual system under all robot
localizations. Using recent manifold learning and deep learning techniques, we
propose a new geometrically motivated solution based on training data
consisting of a finite set of images captured in known locations of the robot.
The solution includes estimation of the robot localization mapping from the
appearance space to the robot localization space, as well as estimation of the
inverse mapping for modeling visual image features. The latter allows solving
the robot localization problem as the Kalman filtering problem.Comment: 7 pages, 3 figures, ICMLA 2017 conferenc
Dynamic Topological Mapping with Biobotic Swarms
In this paper, we present an approach for dynamic exploration and mapping of
unknown environments using a swarm of biobotic sensing agents, with a
stochastic natural motion model and a leading agent (e.g., an unmanned aerial
vehicle). The proposed robust mapping technique constructs a topological map of
the environment using only encounter information from the swarm. A sliding
window strategy is adopted in conjunction with a topological mapping strategy
based on local interactions among the swarm in a coordinate-free fashion to
obtain local maps of the environment. These maps are then merged into a global
topological map which can be visualized using a graphical representation that
integrates geometric as well as topological feature of the environment.
Localized robust topological features are extracted using tools from
topological data analysis. Simulation results have been presented to illustrate
and verify the correctness of our dynamic mapping algorithm
Semi-Definite Programming Relaxation for Non-Line-of-Sight Localization
We consider the problem of estimating the locations of a set of points in a
k-dimensional euclidean space given a subset of the pairwise distance
measurements between the points. We focus on the case when some fraction of
these measurements can be arbitrarily corrupted by large additive noise. Given
that the problem is highly non-convex, we propose a simple semidefinite
programming relaxation that can be efficiently solved using standard
algorithms. We define a notion of non-contractibility and show that the
relaxation gives the exact point locations when the underlying graph is
non-contractible. The performance of the algorithm is evaluated on an
experimental data set obtained from a network of 44 nodes in an indoor
environment and is shown to be robust to non-line-of-sight errors
The Manifold Particle Filter for State Estimation on High-dimensional Implicit Manifolds
We estimate the state a noisy robot arm and underactuated hand using an
Implicit Manifold Particle Filter (MPF) informed by touch sensors. As the robot
touches the world, its state space collapses to a contact manifold that we
represent implicitly using a signed distance field. This allows us to extend
the MPF to higher (six or more) dimensional state spaces. Earlier work (which
explicitly represents the contact manifold) only shows the MPF in two or three
dimensions. Through a series of experiments, we show that the implicit MPF
converges faster and is more accurate than a conventional particle filter
during periods of persistent contact. We present three methods of sampling the
implicit contact manifold, and compare them in experiments.Comment: 10 pages. Conference submission pre-print. Work in progres
Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach
Evolving Internet-of-Things (IoT) applications often require the use of
sensor-based indoor tracking and positioning, for which the performance is
significantly improved by identifying the type of the surrounding indoor
environment. This identification is of high importance since it leads to higher
localization accuracy. This paper presents a novel method based on a cascaded
two-stage machine learning approach for highly-accurate and robust localization
in indoor environments using adaptive selection and combination of RF features.
In the proposed method, machine learning is first used to identify the type of
the surrounding indoor environment. Then, in the second stage, machine learning
is employed to identify the most appropriate selection and combination of RF
features that yield the highest localization accuracy. Analysis is based on
k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset
generated from practical measurements of the RF signal in realistic indoor
environments. Received Signal Strength, Channel Transfer Function, and
Frequency Coherence Function are the primary RF features being explored and
combined. Numerical investigations demonstrate that prediction based on the
concatenation of primary RF features enhanced significantly as the localization
accuracy improved by at least 50% to more than 70%.Comment: 13 page
A taxonomy of localization techniques based on multidimensional scaling
Localization in Wireless Sensor Networks (WSNs) has been a challenging
problem in the last decade. The most explored approaches for this purpose are
based on multidimensional scaling (MDS) technique. The first algorithm that
introduced MDS for nodes localization in sensor networks is well known as
MDS-MAP. Since its appearance in 2003, many variations of MDS-MAP have been
proposed in the literature. This paper aims to provide a comprehensive survey
of the localization techniques that are based on MDS. We classify MDS-based
algorithms according to different taxonomy features and different evaluation
metrics
Geometric Learning and Topological Inference with Biobotic Networks: Convergence Analysis
In this study, we present and analyze a framework for geometric and
topological estimation for mapping of unknown environments. We consider agents
mimicking motion behaviors of cyborg insects, known as biobots, and exploit
coordinate-free local interactions among them to infer geometric and
topological information about the environment, under minimal sensing and
localization constraints. Local interactions are used to create a graphical
representation referred to as the encounter graph. A metric is estimated over
the encounter graph of the agents in order to construct a geometric point cloud
using manifold learning techniques. Topological data analysis (TDA), in
particular persistent homology, is used in order to extract topological
features of the space and a classification method is proposed to infer robust
features of interest (e.g. existence of obstacles). We examine the asymptotic
behavior of the proposed metric in terms of the convergence to the geodesic
distances in the underlying manifold of the domain, and provide stability
analysis results for the topological persistence. The proposed framework and
its convergences and stability analysis are demonstrated through numerical
simulations and experiments
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging
from biology and communications to ocean and Earth science. We survey the
recent advances and transformative potential of machine learning (ML),
including deep learning, in the field of acoustics. ML is a broad family of
techniques, which are often based in statistics, for automatically detecting
and utilizing patterns in data. Relative to conventional acoustics and signal
processing, ML is data-driven. Given sufficient training data, ML can discover
complex relationships between features and desired labels or actions, or
between features themselves. With large volumes of training data, ML can
discover models describing complex acoustic phenomena such as human speech and
reverberation. ML in acoustics is rapidly developing with compelling results
and significant future promise. We first introduce ML, then highlight ML
developments in four acoustics research areas: source localization in speech
processing, source localization in ocean acoustics, bioacoustics, and
environmental sounds in everyday scenes.Comment: Published with free access in Journal of the Acoustical Society of
America, 27 Nov. 201
Outlier Detection and Optimal Anchor Placement for 3D Underwater Optical Wireless Sensor Networks Localization
Location is one of the basic information required for underwater optical
wireless sensor networks (UOWSNs) for different purposes such as relating the
sensing measurements with precise sensor positions, enabling efficient
geographic routing techniques, and sustaining link connectivity between the
nodes. Even though various two-dimensional UOWSNs localization methods have
been proposed in the past, the directive nature of optical wireless
communications and three-dimensional (3D) deployment of sensors require to
develop 3D underwater localization methods. Additionally, the localization
accuracy of the network strongly depends on the placement of the anchors.
Therefore, we propose a robust 3D localization method for partially connected
UOWSNs which can accommodate the outliers and optimize the placement of the
anchors to improve the localization accuracy. The proposed method formulates
the problem of missing pairwise distances and outliers as an optimization
problem which is solved through half quadratic minimization. Furthermore,
analysis is provided to optimally place the anchors in the network which
improves the localization accuracy. The problem of optimal anchor placement is
formulated as a combination of Fisher information matrices for the sensor nodes
where the condition of D-optimality is satisfied. The numerical results
indicate that the proposed method outperforms the literature substantially in
the presence of outliers.Comment: 14 pages, 11 figures, Accepted for Publication in IEEE Transactions
on Communication
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