Drones, Volume 2, Issue 1 (March 2018) – 11 articles

During the past several decades, there have been many reports of sightings of the ivory-billed woodpecker (Campephilus principalis), but nobody has managed to obtain a clear photo, which is regarded as the standard form of evidence for documenting birds. A study was conducted in the Pearl River swamp in southeastern Louisiana to test the feasibility of searching for this elusive species and surveying its habitat using a DJI Phantom 3 Professional drone with a 4K video camera. Drone images are of much higher quality than images that were previously obtained at much greater expense during flights in a Cessna 172. The approach was found to be effective for searching for and inspecting trees that are potential foraging sites for woodpeckers and that might be suitable for nest and roost cavities. Large woodpeckers in flight are identifiable in video footage obtained from an altitude of 40 m, which was found to be sufficient to reliably avoid collisions with trees in the study area. Full article

Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path. Full article

This paper introduces a method to efficiently monitor the status of a piston engine during flight. ECUs (Electronic Control Units) make it possible to fly safely without emergencies or urgencies with random electronic failures of components and connections. The same can be easily done on older engines by adding a reliable digital monitoring system and an automated calibration of the carburetors. In fact, their reliability is several order of magnitude inferior to modern turboshafts. In modern engines with FADEC (Full Authority Digital Electronic Control) as the “on” button is pressed the sensors and actuators are checked. The CPUs will then run start-up during the cranking phase (engine running without ignition). If everything is all right, then the engine starts and the post start checks are also performed. During flight, the ECU checks CPUs, sensors and actuators. Therefore, the electronic system can be monitored with high reliability without much effort. The sensors may crosscheck the engine situation and may output very reliable early diagnosis of incoming failures. Statistical data on spare parts are invaluable for monitoring application, signaling weak or not-lasting components and failure modes. This is another advantage of automotive piston engines conversions to aircraft use. Full article

Refineries’ structures require constant inspection, maintenance of their structural health condition, and safety of the users; however, accessing these structures is getting more and more difficult due to their enormous height and size. In order to deal with this problem, many researchers have developed several robots for wall crawling, yet there is no guaranteed solution. One of the critical reasons why existing wall-crawling robots have not been available in the field is the risk of accidental fall due to operational failure from the harsh environment, like strong wind and the surface’s unpredictable condition. Therefore, we attempted to develop a wall-sticking aerial robot platform that can approach any place of the structure by flying and sticking to the target place. The robot is equipped with electro-magnetic hold mount elements to stick the sensor probe on the surface of the structure. This paper deals with installing the wall-sticking mechanism on the aerial robot. Full article

The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN (SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set. Full article

Inertial measurement units (IMUs) are fundamental for attitude control of drones. With the advancements in micro-electro-mechanical systems (MEMS) fabrication processes, size, power consumption, and price of these sensors have reduced significantly and attracted many new applications. However, this came at the expense of sensors requiring frequent recalibration, as they are highly contaminated with systematic errors. This paper presents a novel method to jointly calibrate the accelerometer, gyroscope, and magnetometer triad in a MEMS IMU without additional equipment. Opportunistic zero change in velocity and position updates, and inclination updates were used in conjunction with relative orientation updates from magnetometers in a robust batch least-squares adjustment. Solutions from the proposed self-calibration were compared to existing calibration methods. Empirical results suggest that the new method is robust against magnetic distortions and can achieve performance similar to a specialized calibration that uses a more accurate (and expensive) IMU as reference. The jointly estimated accelerometer and gyroscope calibration parameters can deliver a more accurate dead-reckoning solution than the popular multi-position calibration method (i.e., 54% improvement in orientation accuracy) by recovering the gyroscope scale error and other systematic errors. In addition, it can improve parameter observability as well as reduce calibration time by incorporating dynamic data with static orientations. The proposed calibration was also applied on-site pre-mission by simply waving the sensor by hand and was able to improve the orientation tracking accuracy by 73%. Full article

Close-range photogrammetry as a technique to acquire reality-based 3D data has, in recent times, seen a renewed interest due to developments in sensor technologies. Furthermore, the strong democratization of UAVs (Unmanned Aerial Vehicles) or drones means that close-range photogrammetry can now be used as a viable low-cost method for 3D mapping. In terms of software development, this led to the creation of many commercial black-box solutions (PhotoScan, Pix4D, etc.). This paper aims to demonstrate how the open source toolbox DBAT (Damped Bundle Adjustment Toolbox) can be used to generate detailed photogrammetric network diagnostics to help assess the quality of surveys processed by the commercial software, PhotoScan. In addition, the Apero module from the MicMac software suite was also used to provide an independent assessment of the results. The assessment is performed by the careful examination of some of the bundle adjustment metrics generated by both open source solutions. A UAV project was conducted on a historical church in the city center of Strasbourg, France, in order to provide a dataset with a millimetric level of precision. Results showed that DBAT can be used to reprocess PhotoScan projects under similar conditions and derive useful metrics from them, while Apero provides a completely independent verification of the results of commercial solutions. Overall, this paper shows that an objective assessment of photogrammetric results is important. In cases where problems are encountered in the project, this assessment method can be useful to detect errors that may not be explicitly presented by PhotoScan. Full article

The following paper presents results obtained from experiments conducted to investigate the viability of acoustic sensing to form the basis of a non-cooperative aircraft collision avoidance system. An unmanned aerial vehicle (UAV) fitted with two microphones was flown in the vicinity of another airborne UAV to determine the maximum distance at which the intruding aircraft could be detected. A two-dimensional analytical model to approximate the minimum detection distance required to facilitate an avoidance maneuver for a given spatial configuration is presented. A method to increase detection distances by exploiting the harmonic nature of acoustic signals generated by propeller-driven aircraft is also presented. The method significantly increases the detection distances compared to the commonly used incoherent spectral mean. It was found that a small gasoline-powered UAV could be detected at distances up to 678 m, which is more than double the minimum required to avoid a head-on collision. Full article

Historic Environment Scotland (HES) is the lead public body which investigates, promotes and cares for the historic environment in Scotland. It undertakes a range of archaeological airborne work from detailed documentation of individual sites to extensive national programmes of prospection. In undertaking this work HES draws on a variety of aerial platforms to collect imagery, including light aircraft and unmanned aerial vehicles (UAV—used throughout this paper as an umbrella term). In all cases, the archaeological questions at hand are the key driver for choice of methodology and platforms, recognising that different types of survey and documentation demand different responses. Differing strands of aerial work will be briefly described, followed by short case studies that illustrate the range of our activities, concluding with thoughts on the context of UAV work for archaeological applications. Full article

The following paper addresses the issue of performing CFAR detection on signals with colored noise distributions, such as that found when performing acoustic sensing via UAVs. With respect to the outlined considerations, a CFAR-enhanced spectral whitening method is proposed to maintain detector functionality without inhibiting detection sensitivity. The performance of the method is also demonstrated using acoustic data taken from experiments involving a fixed-wing UAV. From the results obtained, it is evident the approach performs significantly better than standard techniques such as inverse spectral whitening, which tend to attenuate acquired target source components. Full article