Experimental study on the dynamic behaviour of drones designed for racing competitions

[EN] Drones designed for racing usually feature powerful miniaturised electronics embedded in fairly light and strong geometric composite structures. The main objective of this article is to analyse the behaviour of various models of racing drones and their geometrical structures (airframes). Two approaches have been made: (i) an analysis of the information collected by a set of speed and time sensors located on an indoor race track and using a statistical technique (box and whiskers diagram) and (ii) an analysis of the know-how (flight sensations) of a group of racing pilots using a series of technical interviews on the behaviour of their drones. By contrasting these approaches, it has been possible to validate numerically the effects of varying the arm angles, as well as lengths, on a test race track and relate the geometry of these structures to racing behaviourThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by project RTI2018-096904-B-I00 from the Spanish Ministry of Economy, and by project AICO/2019/055 from Generalitat Valenciana.Castiblanco Quintero, JM.; Garcia-Nieto, S.; Simarro Fernández, R.; Salcedo-Romero-De-Ávila, J. (2021). Experimental study on the dynamic behaviour of drones designed for racing competitions. International Journal of Micro Air Vehicles. 13:1-22. https://doi.org/10.1177/175682932110057571221

BabelCalib: A Universal Approach to Calibrating Central Cameras

Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parameters of the used camera model. This might occur because a simpler projection model is assumed in an initial step, or a poor initial guess for the internal parameters is pre-defined. A lot of the difficulties of general camera calibration lie in the use of a forward projection model. We side-step these challenges by first proposing a solver to calibrate the parameters in terms of a back-projection model and then regress the parameters for a target forward model. These steps are incorporated in a robust estimation framework to cope with outlying detections. Extensive experiments demonstrate that our approach is very reliable and returns the most accurate calibration parameters as measured on the downstream task of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib

Toward a Framework for Systematically Categorizing Future UAS Threat Space

Title from PDF of title page, viewed September 21, 2022Dissertation advisor: Travis FieldsVitaIncludes bibliographical references (pages 241-270)Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Electrical and Computer Engineering. University of Missouri--Kansas City, 2021The development of unmanned aerial vehicles (UAVs) is occurring as fast or faster than any other innovation throughout the course of human history. Building an effective means of defending against threats posed by malicious applications of novel technology is imperative in the current global landscape. Gone are the days where the enemy and the threat it poses are well defined and understood. Defensive technologies have to be modular and able to adapt to a threat technology space which is likely to recycle several times over during the course of a single defense system acquisition cycle. This manuscript wrestles with understanding the unique threat posed by UAVs and related technologies. A thorough taxonomy of the problem is given including projections for how the defining characteristics of the problem are likely to change and grow in the near future. Next, a discussion of the importance of tactics related to the problem of a rapidly changing threat space is provided. A discussion of case studies related to lessons learned from military acquisition programs and pivotal technological innovations in the course of history are given. Multiple measures of success are proposed which are designed to allow for meaningful comparisons and honest evaluations of capabilities. These measures are designed to facilitate discussions by providing a common, and comprehensible language that accounts for the vast complexity of the problem space without getting bogged down by the details. Lastly, predictions for the future threat space comprising UAVs is given. The contributions of this work are thus threefold. Firstly, an analytic framework is presented including a detailed parameterization of the problem as well as various solution techniques borrowed from a variety of fields. Secondly, measures of success are presented which attempt to compare the effectiveness of various systems by converting to expected values in terms of effective range, or extending the popular concept of kill chain and collapsing effectiveness into units of time. A novel technique for measuring effectiveness is presented whereby effectiveness is composed of various individual probabilities. Probabilities and associated distributions can be combined according to the rules of joint probabilities and distributions and allows performance against a probabilistic threat to be measured succinctly and effectively. The third contribution concerns predictions made with respect to the UAS threat space in the future. These predictions are designed to allow for defensive systems to be developed with a high expected effectiveness against current and future threats. Essentially this work comprises a first attempt toward developing a complete framework related to engagement and mission level modeling of a generic defensive system (or combination of systems) in the face of current and future threats presented by UAS.Introduction -- Literature review -- War gaming -- Measures of success -- Conclusion

UAS Pilots Code – Annotated Version 1.0

The UAS PILOTS CODE (UASPC) offers recommendations to advance flight safety, ground safety, airmanship, and professionalism.6 It presents a vision of excellence for UAS pilots and operators, and includes general guidance for all types of UAS. The UASPC offers broad guidance—a set of values—to help a pilot interpret and apply standards and regulations, and to confront real world challenges to avoid incidents and accidents. It is designed to help UAS pilots develop standard operating procedures (SOPs), effective risk management,7 safety management systems (SMS), and to encourage UAS pilots to consider themselves aviators and participants in the broader aviation community

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%