Computer vision in target pursuit using a UAV

Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input. As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone. In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search. To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations. Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects

A survey on acoustic positioning systems for location-based services

Positioning systems have become increasingly popular in the last decade for location-based services, such as navigation, and asset tracking and management. As opposed to outdoor positioning, where the global navigation satellite system became the standard technology, there is no consensus yet for indoor environments despite the availability of different technologies, such as radio frequency, magnetic field, visual light communications, or acoustics. Within these options, acoustics emerged as a promising alternative to obtain high-accuracy low-cost systems. Nevertheless, acoustic signals have to face very demanding propagation conditions, particularly in terms of multipath and Doppler effect. Therefore, even if many acoustic positioning systems have been proposed in the last decades, it remains an active and challenging topic. This article surveys the developed prototypes and commercial systems that have been presented since they first appeared around the 1980s to 2022. We classify these systems into different groups depending on the observable that they use to calculate the user position, such as the time-of-flight, the received signal strength, or the acoustic spectrum. Furthermore, we summarize the main properties of these systems in terms of accuracy, coverage area, and update rate, among others. Finally, we evaluate the limitations of these groups based on the link budget approach, which gives an overview of the system's coverage from parameters such as source and noise level, detection threshold, attenuation, and processing gain.Agencia Estatal de InvestigaciónResearch Council of Norwa

Lidar-based scale recovery dense SLAM for UAV navigation

Imagine of having an autonomous agent (drone, robot, car, ..) that wants to navigate inside an unknown environment. The first question that it needs to answer for accomplish such task is: where Am I? Where are the objects that are surrounding me? The SLAM algorithm can answer to both questions simultaneously, in an on-line manner. This thesis focus on the implementation of a monocular SLAM algorithm on the UAV framework, where the classical obtained sparsity map is densified by means of a Convolutional Neural Network, properly scaled through 2D lidar measurements.Imagine of having an autonomous agent (drone, robot, car, ..) that wants to navigate inside an unknown environment. The first question that it needs to answer for accomplish such task is: where Am I? Where are the objects that are surrounding me? The SLAM algorithm can answer to both questions simultaneously, in an on-line manner. This thesis focus on the implementation of a monocular SLAM algorithm on the UAV framework, where the classical obtained sparsity map is densified by means of a Convolutional Neural Network, properly scaled through 2D lidar measurements

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Change detection of urban vegetation from terrestrial laser scanning and drone photogrammetry

Urban areas experience continuous transformations, impacting the urban vegetation, particularly urban trees. The expansion of urban landscapes directly impacts green spaces and vegetation within cities. Urban vegetation plays a crucial role in improving the urban environment, benefiting residents' well-being, air quality, and temperature regulation. Monitoring changes in urban vegetation is therefore essential, considering the environmental and well-being aspects. This study focuses on change detection using terrestrial laser scanning (TLS) and drone photogrammetry, utilizing three-dimensional (3D) point cloud data. Change detection compares multi-temporal datasets to analyze variations in a geographic region. TLS and drone photogrammetry techniques have gained popularity for monitoring urban vegetation, as they enable the acquisition of detailed 3D information. Point cloud data captures 3D information, enabling detailed change detection and 3D visualization of urban vegetation. This enhances the level of detail and information provided by the methodologies. The objective is to estimate the growth of urban vegetation in a specific area within Helsinki's Malminkartano region during the spring and fall seasons of 2022 using multi-temporal TLS, UAV photogrammetry, and their integration. The research examines the suitability of different point cloud datasets acquired with different sensors and parameters for change detection analysis, identifying potential differences, challenges, and proposed solutions. Three distinct methods, namely C2C, C2M, and M3C2 are employed for point cloud comparison. The results highlighted that manual processing is required to make the point cloud datasets comparable, with significant issues related to differences in point density and resolution. The sparser UAV photogrammetry datasets pose limitations on detailed analysis for change detection. The visual results reveal that TLS datasets detect changes in urban vegetation up to 2m, while UAV photogrammetry and integrated datasets up to 2.8m. However, applying a threshold at a 95% confidence level, 80-90% of significant changes in TLS datasets are observed up to 0.5m, up to 1m in UAV datasets, and up to 0.5m in integrated datasets. These changes represent the growth of urban vegetation during the leaf-off and leaf-on seasons examined. Overall, the utilized datasets provide valuable insights into changes in urban vegetation within the study area