A Grid-based Sensor Floor Platform for Robot Localization using Machine
Learning
- URL: http://arxiv.org/abs/2212.04721v1
- Date: Fri, 9 Dec 2022 08:29:50 GMT
- Title: A Grid-based Sensor Floor Platform for Robot Localization using Machine
Learning
- Authors: Anas Gouda, Danny Heinrich, Mirco H\"unnefeld, Irfan Fachrudin
Priyanta, Christopher Reining, Moritz Roidl
- Abstract summary: We investigate machine learning methods using a new grid-based WSN platform called Sensor Floor.
Our goal is to localize all logistic entities, for this study we use a mobile robot.
The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Wireless Sensor Network (WSN) applications reshape the trend of warehouse
monitoring systems allowing them to track and locate massive numbers of
logistic entities in real-time. To support the tasks, classic Radio Frequency
(RF)-based localization approaches (e.g. triangulation and trilateration)
confront challenges due to multi-path fading and signal loss in noisy warehouse
environment. In this paper, we investigate machine learning methods using a new
grid-based WSN platform called Sensor Floor that can overcome the issues.
Sensor Floor consists of 345 nodes installed across the floor of our logistic
research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors.
Our goal is to localize all logistic entities, for this study we use a mobile
robot. We record distributed sensing measurements of Received Signal Strength
Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon
system as the ground truth. The asynchronous collected data is pre-processed
and trained using Random Forest and Convolutional Neural Network (CNN). The CNN
model with regularization outperforms the Random Forest in terms of
localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can
be configured flexibly depending on the scenario in the warehouse. The
hardware, software and the CNN architecture of the Sensor Floor are open-source
under https://github.com/FLW-TUDO/sensorfloor.
Related papers
- Improved Indoor Localization with Machine Learning Techniques for IoT
applications [0.0]
This study employs machine learning algorithms in three phases: supervised regressors, supervised classifiers, and ensemble methods for RSSI-based indoor localization.
The experiment's outcomes provide insights into the effectiveness of different supervised machine learning techniques in terms of localization accuracy and robustness in indoor environments.
arXiv Detail & Related papers (2024-02-18T02:55:19Z) - A distributed neural network architecture for dynamic sensor selection
with application to bandwidth-constrained body-sensor networks [53.022158485867536]
We propose a dynamic sensor selection approach for deep neural networks (DNNs)
It is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset.
We show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
arXiv Detail & Related papers (2023-08-16T14:04:50Z) - Efficient Model Adaptation for Continual Learning at the Edge [15.334881190102895]
Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment.
Data distributions often shift over time due to changes in environmental factors, sensor characteristics, and task-of-interest.
This paper presents theAdaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
arXiv Detail & Related papers (2023-08-03T23:55:17Z) - Leveraging arbitrary mobile sensor trajectories with shallow recurrent
decoder networks for full-state reconstruction [4.243926243206826]
We show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic information can be mapped to full state-space estimates.
The exceptional performance of the network architecture is demonstrated on three applications.
arXiv Detail & Related papers (2023-07-20T21:42:01Z) - UnLoc: A Universal Localization Method for Autonomous Vehicles using
LiDAR, Radar and/or Camera Input [51.150605800173366]
UnLoc is a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions.
Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets.
arXiv Detail & Related papers (2023-07-03T04:10:55Z) - Automated classification of pre-defined movement patterns: A comparison
between GNSS and UWB technology [55.41644538483948]
Real-time location systems (RTLS) allow for collecting data from human movement patterns.
The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
arXiv Detail & Related papers (2023-03-10T14:46:42Z) - iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
iSDF is a continuous learning system for real-time signed distance field reconstruction.
It produces more accurate reconstructions and better approximations of collision costs and gradients.
arXiv Detail & Related papers (2022-04-05T15:48:39Z) - Neural Inertial Localization [24.854242481051383]
We present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations.
We developed a solution, dubbed neural inertial localization (NILoc) which uses a neural inertial navigation technique to turn sensor history to a sequence of velocity vectors.
Our approach is significantly faster and competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower.
arXiv Detail & Related papers (2022-03-29T18:45:27Z) - Neural RF SLAM for unsupervised positioning and mapping with channel
state information [51.484516640867525]
We present a neural network architecture for jointly learning user locations and environment mapping up to isometry.
The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder.
arXiv Detail & Related papers (2022-03-15T21:32:44Z) - Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for
Multi-Robot Systems [92.26462290867963]
Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
arXiv Detail & Related papers (2021-06-28T03:56:40Z) - Wireless Localisation in WiFi using Novel Deep Architectures [4.541069830146568]
This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding.
We present a novel shallow neural network (SNN) in which features are extracted from the channel state information corresponding to WiFi subcarriers received on different antennas.
arXiv Detail & Related papers (2020-10-16T22:48:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.