Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled)

Improving Optimization of Convolutional Neural Networks through Parameter Fine-tuning

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter fine-tuning over random initialization. We find that training should not be reduced after transferring weights, larger, more similar networks tend to be the best source task, and parameter fine-tuning can often outperform randomly initialized ensembles. The experimental framework and findings will help to train models with improved accuracy

Cyber Space Odyssey: A Competitive, Team-Oriented Serious Game in Computer Networking

Cyber Space Odyssey (CSO) is a novel serious game supporting computer networking education by engaging students in a race to successfully perform various cybersecurity tasks in order to collect clues and solve a puzzle in virtual near-Earth 3D space. Each team interacts with the game server through a dedicated client presenting a multimodal interface, using a game controller for navigation and various desktop computer networking tools of the trade for cybersecurity tasks on the game\u27s physical network. Specifically, teams connect to wireless access points, use packet monitors to intercept network traffic, decrypt and reverse engineer that traffic, craft well-formed and meaningful responses, and transmit those responses. Successful completion of these physical network actions to solve a sequence of increasingly complex problems is necessary to progress through the virtual, story-driven adventure. Use of the networking tools reinforces networking theory and offers hands-on practical training requisite for today\u27s cyberoperators. This paper presents the learning outcomes targeted by a classroom intervention based on CSO, the design and implementation of the game, a pedagogical overview of the overall intervention, and four years of quantitative and qualitative data assessing its effectiveness

Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled)

Combining Stereo Vision and Inertial Navigation for Automated Aerial Refueling

This paper describes the design of an extended Kalman filter to obtain the precise relative position of two aircraft in a refueling maneuver while operating in GPS-denied environments. The extended Kalman filter uses the inertial navigation system already present in both aircraft as well as the stereo camera system organic to new tanker systems. The aircraft trajectories are generated to represent an authentic refueling profile with flight dynamics software and executed in a three-dimensional virtual environment to enable deterministic simulation of the stereo camera system and to demonstrate the effectiveness of the combined system in an authentic refueling scenario. Results show the system can achieve sufficient accuracy using only stereo machine vision and inertial navigation system measurements, though the system is capable of incorporating GPS measurements when available for an additional increase in accuracy

Long-Range Pose Estimation for Aerial Refueling Approaches Using Deep Neural Networks

Automated aerial refueling (AAR) provides unique challenges for computer vision systems. Aerial refueling maneuvers require high-precision low-variance pose estimates. The performance of two stereoscopic (stereo) vision systems is quantified in ground tests specially designed to mimic AAR. In this experiment, three-dimensional (3-D) pose-estimation errors of 6 cm on a target 30 m from the current vision system are achieved. Next, a novel computer vision pipeline to efficiently generate a 3-D point cloud of the target object using stereo vision that leverages a convolutional neural network (CNN) is proposed. Using the proposed approach, a high-fidelity 3-D point cloud with ultra-high-resolution imagery 11.3 times faster than previous approaches can be generated

Delaunay Walk for Fast Nearest Neighbor: Accelerating Correspondence Matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation