The development and application of the Hovermap autonomous flight system are discussed in relation to underground mining, with examples from its early adoption. The current performance of the system and subsequent data interpretation suggest some scenarios in which Hovermap deployment is appropriate. The examples discussed focus principally on improving the detail of observational data from inaccessible areas commonly encountered in underground mines. These insights can then be used in design review and management processes. Recent and future developments in the hardware, software platforms, and the associated data analytics are outlined.

The development and current state of the Hovermap autonomous flight system in underground and GPS-denied areas is discussed, with examples obtained during the development and early adoption of the system.The current performance of the system and subsequent data interpretation suggest some scenarios in which Hovermap deployment is appropriate and have been proven.The examples focus principally on improving safety through a better understanding of the rock mass behaviour and failure mechanisms commonly encountered in deep and high-stress mining conditions, and from feeding these insights back into the design process.Recent and future developments in the hardware and software platforms and the associated data analytics are also outlined

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager's capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor's ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector.

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary andmobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager’s capacity to image the CH4 release from 100m, and a Boreal CH4 laser sensor’s ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector. https://doi.org/10.1016/j.ijggc.2017.11.018 Received 4 August 2017; Received in revised form 14 November 2017; Accepted 18 November 2017 ⁎ Corresponding author at: Geoscience Australia, Canberra, Australia. E-mail address: andrew.feitz@ga.gov.au (A. Feitz). International Journal of Greenhouse Gas Control 70 (2018) 202–224 Available online 15 March 2018 1750-5836/ Crown Copyright © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). T

This paper presents an algorithm and a system for vertical infrastructure inspection using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures such as light and power distribution poles is a difficult task that is time-consuming, dangerous and expensive. Recently, micro VTOL platforms (i.e., quad-, hexa- and octa-rotors) have been rapidly gaining interest in research, military and even public domains. The unmanned, low-cost and VTOL properties of these platforms make them ideal for situations where inspection would otherwise be time-consuming and/or hazardous to humans. There are, however, challenges involved with developing such an inspection system, for example flying in close proximity to a target while maintaining a fixed stand-off distance from it, being immune to wind gusts and exchanging useful information with the remote user. To overcome these challenges, we require accurate and high-update rate state estimation and high performance controllers to be implemented onboard the vehicle. Ease of control and a live video feed are required for the human operator. We demonstrate a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. Two approaches are presented: Position-Based Visual Servoing (PBVS) using an Extended Kalman Filter (EKF) and estimator-free Image-Based Visual Servoing (IBVS). Both use monocular visual, inertia, and sonar data, allowing the approaches to be applied for indoor or GPS-impaired environments. We extensively compare the performances of PBVS and IBVS in terms of accuracy, robustness and computational costs. Results from simulations Sensors 2015, 15 22004 and indoor/outdoor (day and night) flight experiments demonstrate the system is able to successfully inspect and circumnavigate a vertical pole.

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.

Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm2), whereas image-based methods allow the sampling of entire plots (2 to 30 m2). A higher aerial position allows the rapid measurement of large numbers of plots if the altitude is low (10 to 40 m) and the flight control is sufficiently precise to collect high-resolution images. This paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

We present an approach for the inspection of vertical pole-like infrastructure using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures, such as light and power distribution poles, is a time consuming, dangerous and expensive task with high operator workload. To address these issues, we propose a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. We adopt an Image based Visual Servoing (IBVS) technique using only two line features to stabilise the vehicle with respect to a pole. Visual, inertial and sonar data are used, making the approach suitable for indoor or GPS-denied environments. Results from simulation and outdoor flight experiments demonstrate the system is able to successfully inspect and circumnavigate a pole.

We present an evaluation of stereo vision and laser‐based range sensing for rotorcraft unmanned aerial vehicle (RUAV) obstacle avoidance. Our focus is on sensors that are suitable for mini‐RUAV class vehicles in terms of weight and power consumption. The study is limited to the avoidance of large static obstacles such as trees. We compare two commercially available devices that are representative of the state of the art in two‐dimensional scanning laser and stereo‐based sensing. Stereo is evaluated with three different focal length lenses to assess the tradeoff between range resolution and field of view (FOV). The devices are evaluated in the context of obstacle avoidance through extensive flight trials with an RUAV. We discuss the merits and limitations of each sensor type, including sensing range, FOV, accuracy, and susceptibility to lighting conditions. We show that the stereo device fitted with 8‐mm lenses has a better sensing range and vertical FOV than the laser device; however, it relies on careful calibration and is affected by high‐contrast outdoor lighting conditions. The laser has a wider horizontal FOV and is more reliable at detecting obstacles that are within a 20‐m range. Overall the laser produced superior obstacle avoidance performance, with a success rate of 84% compared to 42% for 8‐mm stereo. © 2012 Wiley Periodicals, Inc.

We present a goal-directed 3D reactive obstacle avoidance algorithm specifically designed for Rotorcraft Unmanned Aerial Vehicles (RUAVs) that fly point-to-point type trajectories. The algorithm detects potential collisions within a cylindrical Safety Volume projected ahead of the UAV. This is done in a 3D occupancy map representation of the environment. An expanding elliptical search is performed to find an Escape Point; a waypoint which offers a collision free route past obstacles and towards a goal waypoint. An efficient occupied voxel checking technique is employed which approximates the Safety Volume by a series of spheres, and uses an approximate nearest neighbour search in a Bkd-tree representation of the occupied voxels. Tests show the algorithm can typically find an Escape Point in under 100 ms using onboard UAV processing for a cluttered environment with 20 000 occupied voxels. Successful collision avoidance results are presented from simulation experiments and from flights with an autonomous helicopter equipped with stereo and laser range sensors.

We present a technique for estimating the 6DOF pose of a PTZ camera by tracking a single moving target in the image with known 3D position. This is useful in situations where it is not practical to measure the camera pose directly. Our application domain is estimating the pose of a PTZ camera so that it can be used for automated GPS-based tracking and filming of UAV flight trials. We present results which show the technique is able to localize a PTZ after a short vision-tracked flight, and that the estimated pose is sufficiently accurate for the PTZ to then actively track a UAV based on GPS position data.

Lists the reviewers who contributed to IEEE Robotics & Automation Magazine in 2010.

Autonomous mini-helicopters have been seen as a viable option for aerial-based powerline inspections, however there are numerous research and engineering challenges in developing a system capable of achieving this task in a dependable manner. We have developed an autonomous helicopter as a research platform which will allow us to demonstrate proof-of-concept capabilities for powerline inspections. Through numerous development cycles and from flight test experience we have gained insights into the key challenges in this area. We discuss these insights, describe the helicopter platform and present our research progress in the area of obstacle avoidance for mini-helicopters.

Searching for humans lost in vast stretches of ocean has always been a difficult task. This paper investigates a machine vision system that addresses this problem by exploiting the useful properties of alternate colour spaces. In particular, the paper investigates the fusion of colour information from the HSV, RGB, YCbCr and YIQ colour spaces within the emission matrix of a Hidden Markov Model tracker to enhance video based maritime target detection. The system has shown promising results. The paper also identifies challenges still needing to be met.

We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision-based techniques. Two commonly used vision-based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground-based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T-junctions and 90-deg bends. Experiments on a rotorcraft unmanned aerial vehicle, which was constrained to two-dimensional flight, demonstrate that stereo vision allowed it to detect an obstacle to the front, and optic flow allowed it to turn away from obstacles to the side. We discuss the theory behind these techniques, our experience in implementing them on the robotic platforms, and their suitability to the urban canyon navigation problem. © 2009 Wiley Periodicals, Inc.

We present a technique for high-dynamic range stereo for outdoor mobile robot applications. Stereo pairs are captured at a number of different exposures (exposure bracketing), and combined by projecting the 3D points into a common coordinate frame, and building a 3D occupancy map. We present experimental results for static scenes with constant and dynamic lighting as well as outdoor operation with variable and high contrast lighting conditions.

Searching for humans lost in vast stretches of ocean has always been a difficult task. In this paper, a range of machine vision approaches are investigated as candidate tools to mitigate the risk of human fatigue and complacency after long hours performing these kind of search tasks. Our two-phased approach utilises point target detection followed by temporal tracking of these targets. Four different point target detection techniques and two tracking techniques are evaluated. We also evaluate the use of different colour spaces for target detection. This paper has a particular focus on Hidden Markov Model based tracking techniques, which seem best able to incorporate a priori knowledge about the maritime search problem, to improve detection performance.

We present a synthesis of techniques for rotorcraft UAV navigation through unknown environments which may contain obstacles. D* Lite and Probabilistic Roadmaps are combined for path planning, together with stereo vision for obstacle detection and dynamic path updating. A 3D occupancy map is used to represent the environment, and is updated online using stereo data. The target application is autonomous helicopter-based structure inspections, which require the UAV to fly safely close to the structures it is inspecting. Results are presented from simulation and with real flight hardware mounted onboard a cable array robot, demonstrating successful navigation through unknown environments containing obstacles.

Human maritime search and rescue missions have always been challenging and an element of chance is involved in the detection of survivors at sea. This research is proposing the use of machine vision to assist UAVs to increase the chances of success in locating humans lost at sea. This paper presents an application of current image processing methods for target detection in a synthetic maritime scenario. An evaluation of the algorithm's performance is also provided. The difficulties faced in the automatic detection of human targets in a maritime search environment are also considered. The paper concludes that there is a range of greyscale intensities, approximately 26% based on current data set, where the target was unable to be detected which may limit the applicability of the algorithm. The effect on performance of target intensity level, threshold, τ, and forgetting factor, α, are also investigated.

We consider multi-robot systems that include sensor nodes and aerial or ground robots networked together. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We present a sensor network deployment method using autonomous aerial vehicles and describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for repair, to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.).