Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Automatic Fire Detection Using Computer Vision Techniques for UAV-based Forest Fire Surveillance

Due to their rapid response capability and maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potentials for forest fire surveillance and detection. Over the last decade, it has shown an increasingly strong demand for UAV-based forest fire detection systems, as they can avoid many drawbacks of other forest fire detection systems based on satellites, manned aerial vehicles, and ground equipments. Despite this, the existing UAV-based forest fire detection systems still possess numerous practical issues for their use in operational conditions. In particular, the successful forest fire detection remains difficult, given highly complicated and non-structured environments of forest, smoke blocking the fire, motion of cameras mounted on UAVs, and analogues of flame characteristics. These adverse effects can seriously cause either false alarms or alarm failures. In order to successfully execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, investigations on how to reduce false alarm rates, increase the probability of successful detection, and enhance adaptive capabilities to various circumstances are strongly demanded to improve the reliability and accuracy of forest fire detection system. According to the above-mentioned requirements, this thesis concentrates on the development of reliable and accurate forest fire detection algorithms which are applicable to UAVs. These algorithms provide a number of contributions, which include: (1) a two-layered forest fire detection method is designed considering both color and motion features of fire; it is expected to greatly improve the forest fire detection performance, while significantly reduce the motion of background caused by the movement of UAV; (2) a forest fire detection scheme is devised combining both visual and infrared images for increasing the accuracy and reliability of forest fire alarms; and (3) a learning-based fire detection approach is developed for distinguishing smoke (which is widely considered as an early signal of fire) from other analogues and achieving early stage fire detection

Improving attitude estimation and control of quadrotor systems

[EN] Some improvements in state estimation and control of quadrotors are presented. An efficient fusion algorihtm based on the Kalman filter, which also compensates the time delay in the attitude estimation is developed. Furthermore, a novel control approach is applied with succesful and promising results[ES] En esta tesina se presentan algunas mejoras en la estimación del estado y control de cuadrirrotores. Se desarrolla un algoritmo de fusión eficiente, que además compensa el retardo en la estimación de la orientación. También se consigue aplicar una técnica de control innovadora con buenos y prometedores resultadosSanz Díaz, R. (2014). Improving attitude estimation and control of quadrotor systems. http://hdl.handle.net/10251/56144Archivo delegad

Mixed Reality and Remote Sensing Application of Unmanned Aerial Vehicle in Fire and Smoke Detection

This paper proposes the development of a system incorporating inertial measurement unit (IMU), a consumer-grade digital camera and a fire detection algorithm simultaneously with a nano Unmanned Aerial Vehicle (UAV) for inspection purposes. The video streams are collected through the monocular camera and navigation relied on the state-of-the-art indoor/outdoor Simultaneous Localisation and Mapping (SLAM) system. It implements the robotic operating system (ROS) and computer vision algorithm to provide a robust, accurate and unique inter-frame motion estimation. The collected onboard data are communicated to the ground station and used the SLAM system to generate a map of the environment. A robust and efficient re-localization was performed to recover from tracking failure, motion blur, and frame lost in the data received. The fire detection algorithm was deployed based on the colour, movement attributes, temporal variation of fire intensity and its accumulation around a point. The cumulative time derivative matrix was utilized to analyze the frame-by-frame changes and to detect areas with high-frequency luminance flicker (random characteristic). Colour, surface coarseness, boundary roughness, and skewness features were perceived as the quadrotor flew autonomously within the clutter and congested area. Mixed Reality system was adopted to visualize and test the proposed system in a physical environment, and the virtual simulation was conducted through the Unity game engine. The results showed that the UAV could successfully detect fire and flame, autonomously fly towards and hover around it, communicate with the ground station and simultaneously generate a map of the environment. There was a slight error between the real and virtual UAV calibration due to the ground truth data and the correlation complexity of tracking real and virtual camera coordinate frames

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones