729 research outputs found
Map matching by using inertial sensors: literature review
This literature review aims to clarify what is known about map matching by
using inertial sensors and what are the requirements for map matching, inertial
sensors, placement and possible complementary position technology. The target
is to develop a wearable location system that can position itself within a complex
construction environment automatically with the aid of an accurate building model.
The wearable location system should work on a tablet computer which is running
an augmented reality (AR) solution and is capable of track and visualize 3D-CAD
models in real environment. The wearable location system is needed to support the
system in initialization of the accurate camera pose calculation and automatically
finding the right location in the 3D-CAD model. One type of sensor which does seem
applicable to people tracking is inertial measurement unit (IMU). The IMU sensors
in aerospace applications, based on laser based gyroscopes, are big but provide a
very accurate position estimation with a limited drift. Small and light units such
as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very
popular, but they have a significant bias and therefore suffer from large drifts and
require method for calibration like map matching. The system requires very little
fixed infrastructure, the monetary cost is proportional to the number of users, rather
than to the coverage area as is the case for traditional absolute indoor location
systems.Siirretty Doriast
On Spectral Coexistence of CP-OFDM and FB-MC Waveforms in 5G Networks
Future 5G networks will serve a variety of applications that will coexist on
the same spectral band and geographical area, in an uncoordinated and
asynchronous manner. It is widely accepted that using CP-OFDM, the waveform
used by most current communication systems, will make it difficult to achieve
this paradigm. Especially, CP-OFDM is not adapted for spectral coexistence
because of its poor spectral localization. Therefore, it has been widely
suggested to use filter bank based multi carrier (FB-MC) waveforms with
enhanced spectral localization to replace CP-OFDM. Especially, FB-MC waveforms
are expected to facilitate coexistence with legacy CP-OFDM based systems.
However, this idea is based on the observation of the PSD of FB-MC waveforms
only. In this paper, we demonstrate that this approach is flawed and show what
metric should be used to rate interference between FB-MC and CP-OFDM systems.
Finally, our results show that using FB-MC waveforms does not facilitate
coexistence with CP-OFDM based systems to a high extent.Comment: Manuscript submitted for review to IEEE Transactions on Wireless
Communication
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its
measurement accuracy and insensitivity to illumination compared to cameras, It
is applied onto a broad range of applications, like geodetic engineering, self
driving cars or virtual reality. But the 3D Lidar with multi-beam is very
expensive, and the massive measurements data can not be fully leveraged on some
constrained platforms. The purpose of this paper is to explore the possibility
of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
moreover, the generated 3D map quality is evaluated by our proposed metrics at
the end. The 3D map is constructed in two ways, one way in which the scan is
performed at known positions with an external rotary axis at another plane. The
other way, in which the 2D Lidar for mapping and another 2D Lidar for
localization are placed on a trolley, the trolley is pushed on the ground
arbitrarily. The generated maps by different approaches are converted to
octomaps uniformly before the evaluation. The similarity and difference between
two maps will be evaluated by the proposed metrics thoroughly. The whole
mapping system is composed of several modular components. A 3D bracket was made
for assembling of the Lidar with a long range, the driver and the motor
together. A cover platform made for the IMU and 2D Lidar with a shorter range
but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International
Conference on Advanced Intelligent Mechatronic
Map Matching by Using Inertial Sensors – Literature Review
This literature review aims to clarify what is known about map matching by
using inertial sensors and what are the requirements for map matching, inertial
sensors, placement and possible complementary position technology. The target
is to develop a wearable location system that can position itself within a complex
construction environment automatically with the aid of an accurate building model.
The wearable location system should work on a tablet computer which is running
an augmented reality (AR) solution and is capable of track and visualize 3D-CAD
models in real environment. The wearable location system is needed to support the
system in initialization of the accurate camera pose calculation and automatically
finding the right location in the 3D-CAD model. One type of sensor which does seem
applicable to people tracking is inertial measurement unit (IMU). The IMU sensors
in aerospace applications, based on laser based gyroscopes, are big but provide a
very accurate position estimation with a limited drift. Small and light units such
as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very
popular, but they have a signicant bias and therefore suffer from large drifts and
require method for calibration like map matching. The system requires very little
fixed infrastructure, the monetary cost is proportional to the number of users, rather
than to the coverage area as is the case for traditional absolute indoor location
systems.</p
RSS-based wireless LAN indoor localization and tracking using deep architectures
Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA
Jointly Optimizing Placement and Inference for Beacon-based Localization
The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
Optimizing Indoor Location Based Tracking through Proper Filter Selection and Wireless Sensor Network Design
Indoor positioning system (IPS) is a topic that is coming up more and more for various reasons, such as allowing companies to track important objects using radio frequency identification (RFID) and employees with Bluetooth devices inside a facility. Geofencing is one of the biggest topics with IPS and is meant to limit access to a network in specified areas. Devices that incorporate indoor tracking are not initially precise when objects and employees are on the move. This movement requires devices to have a reliable filter for noise and package lose. For this paper, the comparison between extended Kalman filters and unscented Kalman filter in a controlled environment will help indicate which is ideal for IPS tracking. Both filters will be applied and compared on location accuracy metrics. The proper design of the wireless network is also crucial for having an effective IPS method. This will show the difference in wireless networks and how the initial design will lead to greater chance of success for IPS
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