Review of UAV positioning in indoor environments and new proposal based on US measurements

Este documento se considera que es una ponencia de congresos en lugar de un capítulo de libro.10th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2019) Pisa, Italy, September 30th - October 3rd, 2019The use of unmanned aerial vehicles (UAVs) has increased dramatically in recent years because of their huge potential in both civil and military applications and the decrease in prize of UAVs products. Location detection can be implemented through GNSS technology in outdoor environments, nevertheless its accuracy could be insufficient for some applications. Usability of GNSS in indoor environments is limited due to the signal attenuation as it cross through walls or the absence of line of sight. Considering the big market opportunity of indoor UAVs many researchers are devoting their efforts in the exploration of solutions for their positioning. Indoor UAV applications include location based services (LBS), advertisement, ambient assisted living environments or emergency response. This work is an update survey in UAV indoor localization, so it can provide a guide and technical comparison perspective of different technologies with their main advantages and drawbacks. Finally, we propose an approach based on an ultrasonic local positioning system.Universidad de AlcaláJunta de Comunidades de Castilla-La ManchaMinisterio de Economía, Industria y Competitivida

3D indoor positioning of UAVs with spread spectrum ultrasound and time-of-flight cameras

Este trabajo propone el uso de un sistema híbrido de posicionamiento acústico y óptico en interiores para el posicionamiento 3D preciso de los vehículos aéreos no tripulados (UAV). El módulo acústico de este sistema se basa en un esquema de Acceso Múltiple por División de Código de Tiempo (T-CDMA), en el que la emisión secuencial de cinco códigos ultrasónicos de espectro amplio se realiza para calcular la posición horizontal del vehículo siguiendo un procedimiento de multilateración 2D. El módulo óptico se basa en una cámara de Tiempo de Vuelo (TOF) que proporciona una estimación inicial de la altura del vehículo. A continuación se propone un algoritmo recursivo programado en un ordenador externo para refinar la posición estimada. Los resultados experimentales muestran que el sistema propuesto puede aumentar la precisión de un sistema exclusivamente acústico en un 70-80% en términos de error cuadrático medio de posicionamiento.This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error.• Gobierno de España y Fondos para el Desarrollo Regional Europeo. Proyectos TARSIUS (TIN2015-71564-C4-4-R) (I+D+i), REPNIN (TEC2015-71426-REDT) y SOC-PLC (TEC2015-64835-C3-2-R) (I+D+i) • Junta de Extremadura, Fondos FEDER y Fondo Social Europeo. Proyecto GR15167 y beca predoctoral 45/2016 Exp. PD16030peerReviewe

Micro and macro quadcopter drones for indoor mapping to support disaster management

In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces

A novel distributed architecture for UAV indoor navigation

Abstract In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation System (ROS) for light weight UAV autonomous indoor flight. The proposed framework is shown to be more robust and flexible than common configurations. A flight controller and companion computer running ROS for control and navigation are also included in the section. Both hardware and software diagrams are given to show the complete architecture. Further works will be based on the experimental validation of the proposed configuration by indoor flight tests

Drone heading calculation indoors

Abstract. Aim of this master’s thesis was to study drone flying indoors and propose a drone-implemented system that enables the drone heading calculation. In the outdoors, the heading is calculated effectively with a drone’s sensors but using them indoors is limited. Indoor positioning currently has not both low-cost and reliable solution for drone heading calculating. The differences between indoor flying principles and outdoor flying principles of the drone are described in the beginning of the thesis. Then different ways to determine the drone’s heading indoors and how they compare with one another are discussed. Finally, two different heading calculation methods are implemented and tested. The methods are based on using multiple location measurements on the drone and using machine vision together with machine learning. Both methods are affordable and are evaluated to see if they could enable drone flying indoors. First method gives out potential results based on testing results, but it needs further development to be able to always provide reliable heading. Second method shows poor results based on verification.Dronen lentosuunnan laskenta sisätiloissa. Tiivistelmä. Työn tavoitteena oli tutkia dronen lentämistä sisätiloissa ja ehdottaa sitä varten droneen implementoitavaa systeemiä, joka mahdollistaa dronen suunnan laskennan. Ulkona suuntatieto saadaan dronen sensorien avulla, mutta sisätiloissa niiden tarkkuus ei riitä samalla tavalla. Sisätilapaikannuksessa ei ole olemassa sekä edullista että luotettavaa ratkaisua dronen suunnan laskentaan. Työssä perehdytään aluksi dronen lentämisen periaatteisiin sisätiloissa ja miten ne eroavat ulkona lentämisestä. Sitten kerrotaan erilaisista keinoista määrittää dronen suunta sisätiloissa ja niiden keskinäisestä vertailusta. Lopuksi testataan kahta erilaista suunnan-laskenta-menetelmää, jotka perustuvat paikkatiedon käyttöön ja konenäköön yhdessä koneoppimisen kanssa. Menetelmät ovat edullisia ja niiden sopivuutta dronen sisälennätykseen arvioidaan. Ensimmäinen menetelmä antaa hyviä testituloksia mutta tarvitsee lisää jatkokehitystä, jotta se voisi antaa aina luotettavaa suuntatietoa. Toinen menetelmä antaa heikkoja tuloksia verifioinnin perusteella

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

Comparative Study of Indoor Navigation Systems for Autonomous Flight

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the society and researchers due to the capability to perform in economic, scientific and emergency scenarios, and are being employed in large number of applications especially during the hostile environments. They can operate autonomously for both indoor and outdoor applications mainly including search and rescue, manufacturing, forest fire tracking, remote sensing etc. For both environments, precise localization plays a critical role in order to achieve high performance flight and interacting with the surrounding objects. However, for indoor areas with degraded or denied Global Navigation Satellite System (GNSS) situation, it becomes challenging to control UAV autonomously especially where obstacles are unidentified. A large number of techniques by using various technologies are proposed to get rid of these limits. This paper provides a comparison of such existing solutions and technologies available for this purpose with their strengths and limitations. Further, a summary of current research status with unresolved issues and opportunities is provided that would provide research directions to the researchers of the similar interests

System Identification of a Micro Aerial Vehicle

The purpose of this thesis was to implement an Model Predictive Control based system identification method on a micro-aerial vehicle (DJI Matrice 100) as outlined in a study performed by ETH Zurich. Through limited test flights, data was obtained that allowed for the generation of first and second order system models. The first order models were robust, but the second order model fell short due to the fact that the data used for the model was not sufficient