Ultraviolet camera measurements of passive and explosive (Strombolian) sulphur dioxide emissions at Yasur Volcano, Vanuatu

Here, we present the first ultraviolet (UV) camera measurements of sulphur dioxide (SO2) flux from Yasur volcano, Vanuatu, for the period 6–9 July 2018. These data yield the first direct gas-measurement-derived calculations of explosion gas masses at Yasur. Yasur typically exhibits persistent passive gas release interspersed with frequent Strombolian explosions. We used compact forms of the “PiCam” Raspberry Pi UV camera system powered through solar panels to collect images. Our daily median SO2 fluxes ranged from 4 to 5.1 kg s−1, with a measurement uncertainty of −12.2% to +14.7%, including errors from the gas cell calibration drift, uncertainties in plume direction and distance, and errors from the plume velocity. This work highlights the use of particle image velocimetry (PIV) for plume velocity determination, which was preferred over the typically used cross-correlation and optical flow methods because of the ability to function over a variety of plume conditions. We calculated SO2 masses for Strombolian explosions ranging 8–81 kg (mean of 32 kg), which to our knowledge is the first budget of explosive gas masses from this target. Through the use of a simple statistical measure using the moving minimum, we estimated that passive degassing is the dominant mode of gas emission at Yasur, supplying an average of ~69% of the total gas released. Our work further highlights the utility of UV camera measurements in volcanology, and particularly the benefit of the multiple camera approach in error characterisation. This work also adds to our inventory of gas-based data, which can be used to characterise the spectrum of Strombolian activity across the globe.</jats:p

Neural networks application to divergence-based passive ranging

The purpose of this report is to summarize the state of knowledge and outline the planned work in divergence-based/neural networks approach to the problem of passive ranging derived from optical flow. Work in this and closely related areas is reviewed in order to provide the necessary background for further developments. New ideas about devising a monocular passive-ranging system are then introduced. It is shown that image-plan divergence is independent of image-plan location with respect to the focus of expansion and of camera maneuvers because it directly measures the object's expansion which, in turn, is related to the time-to-collision. Thus, a divergence-based method has the potential of providing a reliable range complementing other monocular passive-ranging methods which encounter difficulties in image areas close to the focus of expansion. Image-plan divergence can be thought of as some spatial/temporal pattern. A neural network realization was chosen for this task because neural networks have generally performed well in various other pattern recognition applications. The main goal of this work is to teach a neural network to derive the divergence from the imagery

Airborne collision scenario flight tests: impact of angle measurement errors on reactive vision-based avoidance control

The future emergence of many types of airborne vehicles and unpiloted aircraft in the national airspace means collision avoidance is of primary concern in an uncooperative airspace environment. The ability to replicate a pilot’s see and avoid capability using cameras coupled with vision based avoidance control is an important part of an overall collision avoidance strategy. But unfortunately without range collision avoidance has no direct way to guarantee a level of safety. Collision scenario flight tests with two aircraft and a monocular camera threat detection and tracking system were used to study the accuracy of image-derived angle measurements. The effect of image-derived angle errors on reactive vision-based avoidance performance was then studied by simulation. The results show that whilst large angle measurement errors can significantly affect minimum ranging characteristics across a variety of initial conditions and closing speeds, the minimum range is always bounded and a collision never occurs

Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

The automation of low-altitude rotorcraft flight depends on the ability to detect, locate, and navigate around obstacles lying in the rotorcraft's intended flightpath. Computer vision techniques provide a passive method of obstacle detection and range estimation, for obstacle avoidance. Several algorithms based on computer vision methods have been developed for this purpose using laboratory data; however, further development and validation of candidate algorithms require data collected from rotorcraft flight. A data base containing low-altitude imagery augmented with the rotorcraft and sensor parameters required for passive range estimation is not readily available. Here, the emphasis is on the methodology used to develop such a data base from flight-test data consisting of imagery, rotorcraft and sensor parameters, and ground-truth range measurements. As part of the data preparation, a technique for obtaining the sensor calibration parameters is described. The data base will enable the further development of algorithms for computer vision-based obstacle detection and passive range estimation, as well as provide a benchmark for verification of range estimates against ground-truth measurements

A high-resolution full-field range imaging system

There exist a number of applications where the range to all objects in a field of view needs to be obtained. Specific examples include obstacle avoidance for autonomous mobile robots, process automation in assembly factories, surface profiling for shape analysis, and surveying. Ranging systems can be typically characterized as being either laser scanning systems where a laser point is sequentially scanned over a scene or a full-field acquisition where the range to every point in the image is simultaneously obtained. The former offers advantages in terms of range resolution, while the latter tend to be faster and involve no moving parts. We present a system for determining the range to any object within a camera's field of view, at the speed of a full-field system and the range resolution of some point laser scans. Initial results obtained have a centimeter range resolution for a 10 second acquisition time. Modifications to the existing system are discussed that should provide faster results with submillimeter resolution