644 research outputs found
Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
In this paper, we present a robust multipath-based localization and mapping
framework that exploits the phases of specular multipath components (MPCs)
using a massive multiple-input multiple-output (MIMO) array at the base
station. Utilizing the phase information related to the propagation distances
of the MPCs enables the possibility of localization with extraordinary accuracy
even with limited bandwidth. The specular MPC parameters along with the
parameters of the noise and the dense multipath component (DMC) are tracked
using an extended Kalman filter (EKF), which enables to preserve the
distance-related phase changes of the MPC complex amplitudes. The DMC comprises
all non-resolvable MPCs, which occur due to finite measurement aperture. The
estimation of the DMC parameters enhances the estimation quality of the
specular MPCs and therefore also the quality of localization and mapping. The
estimated MPC propagation distances are subsequently used as input to a
distance-based localization and mapping algorithm. This algorithm does not need
prior knowledge about the surrounding environment and base station position.
The performance is demonstrated with real radio-channel measurements using an
antenna array with 128 ports at the base station side and a standard cellular
signal bandwidth of 40 MHz. The results show that high accuracy localization is
possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to
the IEEE Transaction on Wireless Communications for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions
Given a network of receivers and transmitters, the process of determining
their positions from measured pseudo-ranges is known as network
self-calibration. In this paper we consider 2D networks with synchronized
receivers but unsynchronized transmitters and the corresponding calibration
techniques,known as TDOA techniques. Despite previous work, TDOA
self-calibration is computationally challenging. Iterative algorithms are very
sensitive to the initialization, causing convergence issues.In this paper, we
present a novel approach, which gives an algebraic solution to three previously
unsolved scenarios. Our solvers can lead to a position error <1.2% and are
robust to noise
Beyond Gr\"obner Bases: Basis Selection for Minimal Solvers
Many computer vision applications require robust estimation of the underlying
geometry, in terms of camera motion and 3D structure of the scene. These robust
methods often rely on running minimal solvers in a RANSAC framework. In this
paper we show how we can make polynomial solvers based on the action matrix
method faster, by careful selection of the monomial bases. These monomial bases
have traditionally been based on a Gr\"obner basis for the polynomial ideal.
Here we describe how we can enumerate all such bases in an efficient way. We
also show that going beyond Gr\"obner bases leads to more efficient solvers in
many cases. We present a novel basis sampling scheme that we evaluate on a
number of problems
A novel joint points and silhouette-based method to estimate 3D human pose and shape
This paper presents a novel method for 3D human pose and shape estimation
from images with sparse views, using joint points and silhouettes, based on a
parametric model. Firstly, the parametric model is fitted to the joint points
estimated by deep learning-based human pose estimation. Then, we extract the
correspondence between the parametric model of pose fitting and silhouettes on
2D and 3D space. A novel energy function based on the correspondence is built
and minimized to fit parametric model to the silhouettes. Our approach uses
sufficient shape information because the energy function of silhouettes is
built from both 2D and 3D space. This also means that our method only needs
images from sparse views, which balances data used and the required prior
information. Results on synthetic data and real data demonstrate the
competitive performance of our approach on pose and shape estimation of the
human body.Comment: Accepted to ICPR 2020 3DHU worksho
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
While the Transformer architecture has become ubiquitous in the machine
learning field, its adaptation to 3D shape recognition is non-trivial. Due to
its quadratic computational complexity, the self-attention operator quickly
becomes inefficient as the set of input points grows larger. Furthermore, we
find that the attention mechanism struggles to find useful connections between
individual points on a global scale. In order to alleviate these problems, we
propose a two-stage Point Transformer-in-Transformer (Point-TnT) approach which
combines local and global attention mechanisms, enabling both individual points
and patches of points to attend to each other effectively. Experiments on shape
classification show that such an approach provides more useful features for
downstream tasks than the baseline Transformer, while also being more
computationally efficient. In addition, we also extend our method to feature
matching for scene reconstruction, showing that it can be used in conjunction
with existing scene reconstruction pipelines.Comment: Accepted to the 26th International Conference on Pattern Recognitio
Tracking the Motion of Box Jellyfish
In this paper we investigate a system for tracking the motion of box jellyfish tripedalia cystophora in a special test setup. The goal is to measure the motor response of the animal given certain visual stimuli. The approach is based on tracking the special sensory structures – the rhopalia – of the box jellyfish from high-speed video sequences. We have focused on a realtime system with simple building blocks in our system. However, using a combination of simple intensity based detection and model based tracking we achieve promising tracking results with up to 95% accuracy
Multiple Offsets Multilateration : A New Paradigm for Sensor Network Calibration with Unsynchronized Reference Nodes
Positioning using wave signal measurements is used in several applications, such as GPS systems, structure from sound and Wifi based positioning. Mathematically, such problems require the computation of the positions of receivers and/or transmitters as well as time offsets if the devices are unsynchronized. In this paper, we expand the previous state-of-the-art on positioning formulations by introducing Multiple Offsets Multilateration (MOM), a new mathematical framework to compute the receivers positions with pseudoranges from unsynchronized reference transmitters at known positions. This could be applied in several scenarios, for example structure from sound and positioning with LEO satellites. We mathematically describe MOM, determining how many receivers and transmitters are needed for the network to be solvable, a study on the number of possible distinct solutions is presented and stable solvers based on homotopy continuation are derived. The solvers are shown to be efficient and robust to noise both for synthetic and real audio data.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed
Visual Entity Linking: A Preliminary Study
In this paper, we describe a system that jointly extracts entities appearing in images and mentioned in their ac- companying captions. As input, the entity linking pro- gram takes a segmented image together with its cap- tion. It consists of a sequence of processing steps: part- of-speech tagging, dependency parsing, and coreference resolution that enables us to identify the entities as well as possible textual relations from the captions. The pro- gram uses the image regions labelled with a set of pre- defined categories and computes WordNet similarities between these labels and the entity names. Finally, the program links the entities it detected across the text and the images. We applied our system on the Segmented and Annotated IAPR TC-12 dataset that we enriched with entity annotations and we obtained a correct as- signment rate of 55.48
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