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

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

Impact of Unmanned Aircraft Regulations on Autonomous Navigation Approaches for Indoor Multi-Rotor Applications — Survey

Demand in unmanned aircraft (UA) technologies for real-world applications have increased over the recent years, driving national aviation authorities to implement weight dependent regulations across all UA operations. Introduction of registration for UA weighing 250g and above as well as other regulatory requirements for heavier UA systems have motivated manufacturers to consider weight as a part of design requirement. Although UA weight is not a major concern for most outdoor applications, weight requirements imposed by aviation authorities further emphasizes the importance to develop smaller and lighter UA for safer indoor or urban operations in GPS denied environments. Comparison across various sensors used for autonomous UA navigation methods suggested that benefits of using vision sensors outweighs other methods since most UA are equipped with onboard cameras and thus does not require retrofitting of additional hardware. In addition, vision sensor data can potentially be used for both navigation and non-navigation tasks resulting in a productive and lightweight UA system that is able to avoid or reduce regulatory burdens for GPS denied UA operations

DEVELOPMENT OF A METHODOLOGY FOR THE EVALUTATION OF UAV-BASED PHOTOGRAMMETRY: IMPLEMENTATION AT AN UNDERGROUND MINE

Autonomous systems in underground mining are increasingly being implemented as tools to collect data in inaccessible areas and improve the safety of mine personnel. There are many areas in the underground mining environment that cannot be accessed by personnel due to the high potential for ground fall and insufficient ground support. By combining unmanned aerial vehicles (UAVs) with technologies such as photogrammetry and LiDAR (Light Detection and Ranging) scanners, 3D point clouds can be created for inaccessible sites. A 3D digital point cloud can provide valuable geotechnical information such as the ability to measure discontinuities, inspect rock conditions, generate accurate volume estimates, and obtain a georeferenced geometry of the inaccessible opening. There are many challenges to operating UAVs and collecting high-quality imagery in underground environments including poor lighting and visibility, dust, water, confined spaces, air turbulence, and a lack of GPS coverage for navigation and stability. Due to the difficult flying conditions and GPS-denied environment, several companies are developing UAVs with LiDARbased simultaneous localization and mapping (SLAM) to enhance the obstacle detection and avoidance capabilities of the platforms and minimize the potential for a collision. The objective of this research was to develop a methodology that can be used to evaluate UAV-based imaging tools designed to fly in underground environments. A series of demonstrations was designed to test the functionalities of available UAVs and to identify the most effective platforms for collecting UAV-based photogrammetric imagery in an underground mine. Each of the four participating teams was challenged to fly their UAV-based systems (Hovermap, Elios, M2, Ranger/Batonomous) in underground drifts and long-hole stopes while capturing high-quality imagery that could be used to create a 3D digital photogrammetric model of the opening. The demonstrations were held at Barrick Gold Corporation’s Golden Sunlight Mine (GSM) in Whitehall, MT. The systems were evaluated based upon the performance of the collision avoidance (or recovery) system in the underground environment and the quality and accuracy of the data provided. By successfully completing the underground flights and demonstrating well-developed SLAM-based collision avoidance, the Hovermap system proved to be the most reliable, robust, and easily controllable system. The Elios system, relying on collision recovery rather than avoidance, is an affordable alternative for flights in difficult environments. The imagery collected by each system was used to generate photogrammetric point clouds using three software packages: Agisoft PhotoScan, Bentley ContextCapture, and Pix4Dmapper. The point clouds were qualitatively compared based on completeness and detail and quantitatively evaluated for accuracy by comparing the geometry of the point cloud to LiDAR scans of the stopes. Based on the results of the qualitative comparison, the point clouds considered in the accuracy evaluation were built using the photogrammetry software Bentley ContextCapture. When the photogrammetric point clouds were compared with the LiDAR point clouds (assumed to be an accurate baseline reference), the mean error values ranged between 0.47 and 2.86 feet. Despite the different conditions and locations in which the imagery was collected for each model, the observed error varies by less than one order of magnitude. Improvements in the coverage and overlap of the imagery as well as in the method used for georeferencing could further increase the accuracy of the photogrammetric point clouds

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

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

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

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

Sensors Utilisation and Data Collection of Underground Mining

This study reviews IMU significance and performance for underground mine drone localisation. This research has designed a Kalman filter which extracts reliable information from raw data. Kalman filter for INS combines different measurements considering estimated errors to produce a trajectory including time, position and attitude. To evaluate the feasibility of the proposed method, a prototype has been designed and evaluated. Experimental results indicate that the designed Kalman filter estimates the internal states of a system