Rodney A. Walker

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAAs small (<10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.

Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors

This paper presents an evaluation of airborne sensors for use in vegetation management in power-line corridors. Three integral stages in the management process are addressed, including the detection of trees, relative positioning with respect to the nearest power line, and vegetation height estimation. Image data, including multispectral and high resolution, are analyzed along with LiDAR data captured from fixed-wing aircraft. Ground truth data are then used to establish the accuracy and reliability of each sensor, thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a pulse-coupled neural network and morphologic reconstruction applied to multispectral imagery. Through testing, it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved root-mean-square-error (rmse) values of 1.4 and 2.1 m for cross-track distance and along-track position, respectively, while direct georeferencing achieved rmse of 3.1 m in both instances. The estimation of pole and tree heights measured with LiDAR had rmse values of 0.4 and 0.9 m, respectively, while stereo matching achieved 1.5 and 2.9 m. Overall, a small number of poles were missed with detection rates of 98% and 95% for LiDAR and stereo matching.

A Vision Based Forced Landing Site Selection System for an Autonomous UAV

This paper presents a system overview of the UAV forced landing site selection system and the results to date. The forced landing problem is a new field of research for UAVs and this paper will show the machine vision approach taken to address this problem. The results are based on aerial imagery collected from a series of flight trials in a Cessna 172. The aim of this research is to locate candidate landing sites for UAV forced landings, from aerial imagery. Output image frames highlight the algorithms selected safe landing locations. The algorithms for the problem use image processing techniques and neural networks for the classification problem. The system is capable of locating areas that are large enough to land in and that are free of obstacles 92.3% ± 2% (95% confidence) of the time. These areas identified are then further classified as to their surface type to a classification accuracy of 90% ± 3% (98% confidence). It should be noted that although the system is being designed primarily for the forced landing problem for UAVs, the research can also be applied to forced landings or glider applications for piloted aircraft.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect new incursions of spores

Fixed Wing UAV Navigation and Control Through Integrated GNSS and Vision

With the rapid deployment of Unmanned Airborne Vehicles (UAVs) into new applications, the pressure to extend the capabilities of current platforms is increasing. Increased capabilities, however, should preferably not come at the cost of increased aircraft size. In order to strive towards a more capable platform, the UAV must become increasingly aware of its current state (control, navigation and health) and surroundings (location of other aircraft, airspace boundaries, weather and terrain). This paper reports on the results of research into providing a new level of situational awareness to the UAV that is low in cost and complexity. In particular the paper investigates the unique benefits that can be obtained from the integration of a GNSS sensor and a forward-looking vision sensor. The motivation for this investigation is the belief that both GNSS and vision will be integral features of future UAV avionics architectures: GNSS to provide basic aircraft navigation; and vision to provide for obstacle, and aircraft collision avoidance. This paper will present results showing that when single-antenna GNSS measurements are combined with information derived from optical flow techniques, a number of unique synergies emerge. Sensor accuracies and simulated flight control results are presented based on a comprehensive Matlab® Simulink® model which creates an optical flow stream based on the simulated flight of an aircraft. The paper establishes the promise of this novel integrated GNSS/Vision Sensor Suite approach for use as a complete UAV sensor package, or as a backup sensor for an inertial navigation system.

Attitude Estimation for a Fixed-Wing Aircraft Using Horizon Detection and Optical Flow

We develop a method for estimating the flight critical parameters of pitch angle, roll angle and the three body rates using horizon detection and optical flow. We achieve this through the use of an image processing front-end to detect candidate horizon lines through the use of morphological image processing and the Hough transform. The optical flow of the image for each candidate line is calculated, and using these measurements, we are able to estimate the body rates of the aircraft. Using an Extended Kalman Filter (EFK), the candidate horizon lines are propagated and tracked through successive image frames, with statistically unlikely horizon candidates eliminated. Results qualitativly describing the performance of the image processing front-end on real datasets are presented, followed by an analysis of the improvement when utilising the motion model of the vehicle.

Control of Aircraft for Inspection of Linear Infrastructure

Inspection aircraft equipped with cameras and other sensors are routinely used for asset location, inspection, monitoring, and hazard identification of oil-gas pipelines, roads, bridges, and power transmission grids. This paper is concerned with automated flight of fixed-wing inspection aircraft to track approximately linear infrastructure. We propose a guidance law approach that seeks to maintain aircraft trajectories with desirable position and orientation properties relative to the infrastructure under inspection. Furthermore, this paper also proposes the use of an adaptive maneuver selection approach, in which maneuver primitives are adaptively selected to improve the aircrafts attitude behavior. We employ an integrated design methodology particularly suited for an automated inspection aircraft. Simulation studies using full nonlinear semicoupled six degree-of-freedom equations of motion are used to illustrate the effectiveness of the proposed guidance and adaptive maneuver selection approaches in realistic flight conditions. Experimental flight test results are given to demonstrate the performance of the design.

The Smart Skies project

The Smart Skies project is an ambitious and world-leading research endeavor exploring the development of key enabling technologies, which support the efficient utilization of airspace by manned and unmanned airspace users. This provides a programmatic description of the research and development of: an automated separation management system; a mobile aircraft tracking system; and aircraft-based sense-and-ad technologies. A summary of the results from a series of real-world flight testing campaigns is also presented.