PERCEPTION FOR SURVEILLANCE: LEARNING SELF-LOCALISATION AND INTRUDERS DETECTION FROM MONOCULAR IMAGES OF AN AERIAL ROBOT IN OUTDOOR URBAN ENVIRONMENTS

Unmanned aerial vehicles (UAVs), more commonly named drones, are one of the most versatile robotic platforms for their high mobility and low-cost design. Therefore, they have been applied to numerous civil applications. These robots generally can complete autonomous or semi-autonomous missions by undertaking complex calculations on their autopilot system based on the sensors' observations to control their attitude and speed and to plan and track a trajectory for navigating in a possibly unknown environment without human intervention. However, to enable higher degrees of autonomy, the perception system is paramount for extracting valuable knowledge that allows interaction with the external world. Therefore, this thesis aims to solve the core perception challenges of an autonomous surveillance application carried out by an aerial robot in an outdoor urban environment. We address a simplified use case of patrolling missions to monitor a confined area around buildings that is supposedly under access restriction. Hence, we identify the main research questions involved in this application context. On the one hand, the drone has to locate itself in a controlled navigation environment, keep track of its pose while flying, and understand the geometrical structure of the 3D scene around it. On the other hand, the surveillance mission entails detecting and localising people in the monitored area. Consequently, we develop numerous methodologies to address these challenging questions. Furthermore, constraining the UAV's sensor array to a monocular RGB camera, we approach the raised problems with algorithms in the computer vision field. First, we train a neural network with an unsupervised learning paradigm to predict the drone ego-motion and the geometrical scene structure. Hence, we introduce a novel algorithm that integrates a model-free epipolar method to adjust online the rotational drift of the trajectory estimated by the trained pose network. Second, we employ an efficient Convolutional Neural Network (CNN) architecture to regress the UAV global metric pose directly from a single colour image. Moreover, we investigate how dynamic objects in the camera field of view affect the localisation performance of such an approach. Following, we discuss the implementation of an object detection network and derive the equations to find the 3D position of the detected people in a reconstructed environment. Next, we describe the theory behind structure-from-motion and use it to recreate a 3D model of a dataset recorded with a drone at the University of Luxembourg's Belval campus. Ultimately, we perform multiple experiments to validate and evaluate our proposed algorithms with other state-of-the-art methodologies. Results show the superiority of our methods in different metrics. Also, in our analysis, we determine the limitations and highlight the benefits of the adopted strategies compared to other approaches. Finally, the introduced dataset provides an additional tool for benchmarking perception algorithms and future application developments

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

Clinical utility of the Gen-Probe amplified Mycobacterium tuberculosis direct test compared with smear and culture for the diagnosis of pulmonary tuberculosis

ObjectiveTo evaluate the clinical efficacy of the Gen-Probe amplified Mycobacterium tuberculosis direct test (AMTD), a recently developed amplification test for the detection of M. tuberculosis complex directly from clinical specimens, for the diagnosis of pulmonary tuberculosis and its suitability for use in a routine microbiology laboratory.MethodsSequential respiratory specimens were tested with AMTD and results were compared with those of acid-fast stain and culture. Performance of AMTD was tested over a 13-month period, using 278 respiratory specimens, from 219 patients, submitted to the microbiology laboratory of our hospital. AMTD's sensitivity, specificity and positive and negative predictive values were determined, with the combination of culture and clinical diagnosis being taken as the standard.ResultsThirty-three specimens were collected from 23 patients with a conclusive diagnosis of pulmonary tuberculosis. Of these specimens, 13 were smear positive, 22 culture positive and 30 AMTD positive. AMTD was more sensitive in detecting pulmonary tuberculosis in patients partially treated but with undiagnosed disease (100%), and in smear-positive disease (100%). The overall sensitivities, specificities and positive and negative predictive values were: 39.4%, 100%, 100%, and 92.4% for staining; 66.7%, 100%, 100% and 95.7% for culture; and 90.9%, 100%, 100%, and 98.8% for AMTD.ConclusionsAMTD is a rapid, reliable and accurate test for the detection of M. tuberculosis complex in respiratory specimens. Repeat testing of those samples whose results fall between 30 000 and 300 000 relative light units, increases test specificity by preventing the majority of false positives

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed

From SLAM to Situational Awareness: Challenges and Survey

The knowledge that an intelligent and autonomous mobile robot has and is able to acquire of itself and the environment, namely the situation, limits its reasoning, decision-making, and execution skills to efficiently and safely perform complex missions. Situational awareness is a basic capability of humans that has been deeply studied in fields like Psychology, Military, Aerospace, Education, etc., but it has barely been considered in robotics, which has focused on ideas such as sensing, perception, sensor fusion, state estimation, localization and mapping, spatial AI, etc. In our research, we connected the broad multidisciplinary existing knowledge on situational awareness with its counterpart in mobile robotics. In this paper, we survey the state-of-the-art robotics algorithms, we analyze the situational awareness aspects that have been covered by them, and we discuss their missing points. We found out that the existing robotics algorithms are still missing manifold important aspects of situational awareness. As a consequence, we conclude that these missing features are limiting the performance of robotic situational awareness, and further research is needed to overcome this challenge. We see this as an opportunity, and provide our vision for future research on robotic situational awareness.Comment: 15 pages, 8 figure

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

Can nanolites enhance eruption explosivity?

Degassing dynamics play a crucial role in controlling the explosivity of magma at erupting volcanoes. Degassing of magmatic water typically involves bubble nucleation and growth, which drive magma ascent. Crystals suspended in magma may influence both nucleation and growth of bubbles. Micron- to centimeter-sized crystals can cause heterogeneous bubble nucleation and facilitate bubble coalescence. Nanometer-scale crystalline phases, so-called “nanolites”, are an underreported phenomenon in erupting magma and could exert a primary control on the eruptive style of silicic volcanoes. Yet the influence of nanolites on degassing processes remains wholly uninvestigated. In order to test the influence of nanolites on bubble nucleation and growth dynamics, we use an experimental approach to document how nanolites can increase the bubble number density and affect growth kinetics in a degassing nanolite-bearing silicic magma. We then examine a compilation of these values from natural volcanic rocks from explosive eruptions leading to the inference that some very high naturally occurring bubble number densities could be associated with the presence of magmatic nanolites. Finally, using a numerical magma ascent model, we show that for reasonable starting conditions for silicic eruptions, an increase in the resulting bubble number density associated with nanolites could push an eruption that would otherwise be effusive into the conditions required for explosive behavior

Cellular Tropism of Human T-Cell Leukemia Virus Type II Is Enlarged to B Lymphocytes in Patients with High Proviral Load

International audienceTo establish the in vivo cellular tropism of human T-cell leukemia virus type II (HTLV-II) in peripheral blood, subpopulations of mononuclear cells isolated from patients with a history of drug abuse and with high proviral load were analyzed by polymerase chain reaction for the presence of the proviral sequences. After purification of cellular subsets by immunomagnetic fractionation of blood cells of an infected patient, HTLV-II DNA was detected in CD4+ and CD8+ T-cells as well as in CD19+ B-cells. A positive PCR signal was obtained for purified B-cells also at limiting dilutions. This observation was confirmed by purifying the B-cell fraction by a two-step immunomagnetic procedure from the peripheral blood of another patient with very high HTLV-II copy number and quantifying the B-cell proviral load by means of competitive PCR. A proviral copy number of 90/100 B-cells was found, demonstrating that the great majority of these cells were infected by HTLV-II in this subject. The results indicate that HTLV-II has a broad host range in some infected individuals, showing an enlargement of cellular tropism to B lymphocytes and suggesting that this expression is associated with an increase in proviral load

A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs

Two typical Unmanned Aerial Vehicles (UAV) countermeasures involve the detection and tracking of the UAV position, as well as of the human pilot; they are of critical importance before taking any countermeasure, and they already obtained strong attention from national security agencies in different countries. Recent advances in computer vision and artificial intelligence are already proposing many visual detection systems from an operating UAV, but they do not focus on the problem of the detection of the pilot of another approaching unauthorized UAV. In this work, a first attempt of proposing a full autonomous pipeline to process images from a flying UAV to detect the pilot of an unauthorized UAV entering a no-fly zone is introduced. A challenging video sequence has been created flying with a UAV in an urban scenario and it has been used for this preliminary evaluation. Experiments show very encouraging results in terms of recognition, and a complete dataset to evaluate artificial intelligence-based solution will be prepared