A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Look, no Beacons! Optimal All-in-One EchoSLAM

We study the problem of simultaneously reconstructing a polygonal room and a trajectory of a device equipped with a (nearly) collocated omnidirectional source and receiver. The device measures arrival times of echoes of pulses emitted by the source and picked up by the receiver. No prior knowledge about the device's trajectory is required. Most existing approaches addressing this problem assume multiple sources or receivers, or they assume that some of these are static, serving as beacons. Unlike earlier approaches, we take into account the measurement noise and various constraints on the geometry by formulating the solution as a minimizer of a cost function similar to \emph{stress} in multidimensional scaling. We study uniqueness of the reconstruction from first-order echoes, and we show that in addition to the usual invariance to rigid motions, new ambiguities arise for important classes of rooms and trajectories. We support our theoretical developments with a number of numerical experiments.Comment: 5 pages, 6 figures, submitted to Asilomar Conference on Signals, Systems, and Computers Websit

Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach

To improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application. The user's position is calculated with a trained neural network system's models. Additionally the influence of various number of measurements from LTE and WiFi networks in the input vector on the positioning accuracy was examined. From the results it can be seen that using hybrid deep learning algorithm with a radio signatures method can result in localization error 24.3 m and 1.9 m lower comparing respectively to the GPS system and standalone deep learning algorithm with a radio signatures method in indoor environment. What is more, the combination of LTE and WiFi signals measurement in an input vector results in better indoor and outdoor as well as floor classification accuracy and less positioning error comparing to the input vector consisting measurements from only LTE network or from only WiFi network

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored

Adaptive indoor positioning system based on locating globally deployed WiFi signal sources

Recent trends in data driven applications have encouraged expanding location awareness to indoors. Various attributes driven by location data indoors require large scale deployment that could expand beyond specific venue to a city, country or even global coverage. Social media, assets or personnel tracking, marketing or advertising are examples of applications that heavily utilise location attributes. Various solutions suggest triangulation between WiFi access points to obtain location attribution indoors imitating the GPS accurate estimation through satellites constellations. However, locating signal sources deep indoors introduces various challenges that cannot be addressed via the traditional war-driving or war-walking methods. This research sets out to address the problem of locating WiFi signal sources deep indoors in unsupervised deployment, without previous training or calibration. To achieve this, we developed a grid approach to mitigate for none line of site (NLoS) conditions by clustering signal readings into multi-hypothesis Gaussians distributions. We have also employed hypothesis testing classification to estimate signal attenuation through unknown layouts to remove dependencies on indoor maps availability. Furthermore, we introduced novel methods for locating signal sources deep indoors and presented the concept of WiFi access point (WAP) temporal profiles as an adaptive radio-map with global coverage. Nevertheless, the primary contribution of this research appears in utilisation of data streaming, creation and maintenance of self-organising networks of WAPs through an adaptive deployment of mass-spring relaxation algorithm. In addition, complementary database utilisation components such as error estimation, position estimation and expanding to 3D have been discussed. To justify the outcome of this research, we present results for testing the proposed system on large scale dataset covering various indoor environments in different parts of the world. Finally, we propose scalable indoor positioning system based on received signal strength (RSSI) measurements of WiFi access points to resolve the indoor positioning challenge. To enable the adoption of the proposed solution to global scale, we deployed a piece of software on multitude of smartphone devices to collect data occasionally without the context of venue, environment or custom hardware. To conclude, this thesis provides learning for novel adaptive crowd-sourcing system that automatically deals with tolerance of imprecise data when locating signal sources

Autonomous Localization Of A Uav In A 3d Cad Model

This thesis presents a novel method of indoor localization and autonomous navigation of Unmanned Aerial Vehicles(UAVs) within a building, given a prebuilt Computer Aided Design(CAD) model of the building. The proposed system is novel in that it leverages the support of machine learning and traditional computer vision techniques to provide a robust method of localizing and navigating a drone autonomously in indoor and GPS denied environments leveraging preexisting knowledge of the environment. The goal of this work is to devise a method to enable a UAV to deduce its current pose within a CAD model that is fast and accurate while also maintaining efficient use of resources. A 3-Dimensional CAD model of the building to be navigated through is provided as input to the system along with the required goal position. Initially, the UAV has no idea of its location within the building. The system, comprising a stereo camera system and an Inertial Measurement Unit(IMU) as its sensors, then generates a globally consistent map of its surroundings using a Simultaneous Localization and Mapping (SLAM) algorithm. In addition to the map, it also stores spatially correlated 3D features. These 3D features are then used to generate correspondences between the SLAM map and the 3D CAD model. The correspondences are then used to generate a transformation between the SLAM map and the 3D CAD model, thus effectively localizing the UAV in the 3D CAD model. Our method has been tested to successfully localize the UAV in the test building in an average of 15 seconds in the different scenarios tested contingent upon the abundance of target features in the observed data. Due to the absence of a motion capture system, the results have been verified by the placement of tags on the ground at strategic known locations in the building and measuring the error in the projection of the current UAV location on the ground with the tag