An Indoor Localization Dataset and Data Collection Framework with High
Precision Position Annotation
- URL: http://arxiv.org/abs/2209.02270v1
- Date: Tue, 6 Sep 2022 07:41:11 GMT
- Title: An Indoor Localization Dataset and Data Collection Framework with High
Precision Position Annotation
- Authors: F. Serhan Dani\c{s}, A. Teoman Naskali, A. Taylan Cemgil, Cem Ersoy
- Abstract summary: The technique implements an augmented reality (AR) based positioning system that is used to annotate the wireless signal parameter data samples.
We track the position of a practical and low cost navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area decorated with AR markers.
Our results show that we can reduce the positional error of the AR localization system to a rate under 0.05 meters.
- Score: 7.152408514130423
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a novel technique and an associated high resolution dataset that
aims to precisely evaluate wireless signal based indoor positioning algorithms.
The technique implements an augmented reality (AR) based positioning system
that is used to annotate the wireless signal parameter data samples with high
precision position data. We track the position of a practical and low cost
navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area
decorated with AR markers. We maximize the performance of the AR-based
localization by using a redundant number of markers. Video streams captured by
the cameras are subjected to a series of marker recognition, subset selection
and filtering operations to yield highly precise pose estimations. Our results
show that we can reduce the positional error of the AR localization system to a
rate under 0.05 meters. The position data are then used to annotate the BLE
data that are captured simultaneously by the sensors stationed in the
environment, hence, constructing a wireless signal data set with the ground
truth, which allows a wireless signal based localization system to be evaluated
accurately.
Related papers
- SOLVR: Submap Oriented LiDAR-Visual Re-Localisation [13.434340164323473]
SOLVR performs place recognition and 6-DoF registration across sensor modalities.
We show that SOLVR achieves state-of-the-art performance for LiDAR-Visual place recognition and registration.
arXiv Detail & Related papers (2024-09-16T12:58:03Z) - Landmark-based Localization using Stereo Vision and Deep Learning in
GPS-Denied Battlefield Environment [1.19658449368018]
This paper proposes a novel framework for localization in non-GPS battlefield environments using only the passive camera sensors.
The proposed method utilizes a customcalibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark recognition.
Experimental results demonstrate that our proposed framework performs better than existing anchorbased DV-Hop algorithms and competes with the most efficient vision-based algorithms in terms of localization error (RMSE)
arXiv Detail & Related papers (2024-02-19T21:20:56Z) - 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) - Velocity-Based Channel Charting with Spatial Distribution Map Matching [4.913210912019975]
Fingerprint-based localization improves positioning performance in challenging, non-line-of-sight (NLoS) dominated indoor environments.
Channel-charting avoids this labeling effort as it implicitly associates relative coordinates to the recorded radio signals.
We propose a novel framework that does not require reference positions to keep the models up to date.
arXiv Detail & Related papers (2023-11-14T09:21:09Z) - Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration [55.41644538483948]
This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
arXiv Detail & Related papers (2023-04-27T20:21:18Z) - Collaborative Learning with a Drone Orchestrator [79.75113006257872]
A swarm of intelligent wireless devices train a shared neural network model with the help of a drone.
The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
arXiv Detail & Related papers (2023-03-03T23:46:25Z) - HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D
Images [58.720142291102135]
We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment.
The dataset is based on the popular Habitat simulator, in which it is possible to generate indoor scenes using both own sensor data and open datasets.
arXiv Detail & Related papers (2022-12-30T12:20:56Z) - LaMAR: Benchmarking Localization and Mapping for Augmented Reality [80.23361950062302]
We introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices.
We publish a benchmark dataset of diverse and large-scale scenes recorded with head-mounted and hand-held AR devices.
arXiv Detail & Related papers (2022-10-19T17:58:17Z) - Radar Image Reconstruction from Raw ADC Data using Parametric
Variational Autoencoder with Domain Adaptation [0.0]
We propose a parametrically constrained variational autoencoder, capable of generating the clustered and localized target detections on the range-angle image.
To circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies.
arXiv Detail & Related papers (2022-05-30T16:17:36Z) - LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning [59.17191114000146]
LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs)
In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit ( CPU) which may be located in the cloud.
Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps.
arXiv Detail & Related papers (2022-02-01T20:27:46Z) - Real-time Outdoor Localization Using Radio Maps: A Deep Learning
Approach [59.17191114000146]
LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task.
We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
arXiv Detail & Related papers (2021-06-23T17:27:04Z)
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.