Download or read book Robust 3D Target Localization Using UAVs with State Uncertainty written by M. Zagar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Video Stabilization and Target Localization Using Feature Tracking with Small UAV Video written by David Linn Johansen and published by . This book was released on 2006 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned Aerial Vehicles (UAVs) equipped with lightweight, inexpensive cameras have grown in popularity by enabling new uses of UAV technology. However, the video retrieved from small UAVs is often unwatchable due to high frequency jitter. Beginning with an investigation of previous stabilization work, this thesis discusses the challenges of stabilizing UAV based video. It then presents a software based computer vision framework and discusses its use to develop a real-time stabilization solution. A novel approach of estimating intended video motion is then presented. Next, the thesis proceeds to extend previous target localization work by allowing the operator to easily identify targets rather than relying solely on color segmentation to improve reliability and applicability in real world scenarios. The resulting approach creates a low cost and easy to use solution for aerial video display and target localization.

Download or read book Video Stabilization and Target Localization Using Feature Tracking with Video from Small UAVs written by David Linn Johansen and published by . This book was released on 2006 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned Aerial Vehicles (UAVs) equipped with lightweight, inexpensive cameras have grown in popularity by enabling new uses of UAV technology. However, the video retrieved from small UAVs is often unwatchable due to high frequency jitter. Beginning with an investigation of previous stabilization work, this thesis discusses the challenges of stabilizing UAV based video. It then presents a software based computer vision framework and discusses its use to develop a real-time stabilization solution. A novel approach of estimating intended video motion is then presented. Next, the thesis proceeds to extend previous target localization work by allowing the operator to easily identify targets rather than relying solely on color segmentation to improve reliability and applicability in real world scenarios. The resulting approach creates a low cost and easy to use solution for aerial video display and target localization.

Download or read book Map Based Localization for Unmanned Aerial Vehicle Navigation written by Julien Francois Li-Chee-Ming and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%.

Download or read book Robust Target Localization and Segmentation written by Omar Arif and published by LAP Lambert Academic Publishing. This book was released on 2010-09 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.

Download or read book Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles written by Sameera S. Ponda and published by . This book was released on 2008 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) The UAV trajectory optimization is performed for stationary targets, dynamic targets and multiple targets, for many different scenarios of vehicle motion constraints. The resulting trajectories show spiral paths taken by the UAV, which focus on increasing the angular separation between measurements and reducing the relative range to the target, thus maximizing the information provided by each measurement and improving the performance of the estimation. The main drawback of information based trajectory design is the dependence of the Fisher Information Matrix on the true target location. This issue is addressed in this project by executing simultaneous target location estimation and UAV trajectory optimization. Two estimation algorithms, the Extended Kalman Filter and the Particle Filter are considered, and the trajectory optimization is performed using the mean value of the target estimation in lieu of the true target location. The estimation and optimization algorithms run in sequence and are updated in real-time. The results show spiral UAV trajectories that increase filter convergence and overall estimation accuracy, illustrating the importance of information-based trajectory design for target localization using small UAVs.

Download or read book Computer Vision and Target Localization Algorithms for Autonomous Unmanned Aerial Vehicles written by James Alton Ross and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Particle Filter based Architecture for Video Target Tracking and Geo location Using Multiple UAVs written by Christopher Sconyers and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.

Download or read book Graph Theoretic Framework Based Cooperative Control and Estimation of Multiple UAVS for Target Tracking written by Mousumi Ahmed and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing the control technique for nonlinear dynamic systems is a signi cant challenge. Approaches to designing a nonlinear controller are studied and an extensive study on backstepping based technique is performed in this research with the purpose of tracking a moving target autonomously. Our main motivation is to explore the controller for cooperative and coordinating unmanned vehicles in a target tracking application. To start with, a general theoretical framework for target tracking is studied and a controller in three dimensional environment for a single UAV is designed. This research is primarily focused on nding a generalized method which can be applied to track almost any reference trajectory. The backstepping technique is employed to derive the controller for a simpli ed UAV kinematic model. This controller can compute three autopilot modes i.e. velocity, ground heading (or course angle), and ight path angle for tracking the unmanned vehicle. Numerical implementation is performed in MATLAB with the assumption of having perfect and full state information of the target to investigate the accuracy of the proposed controller. This controller is then frozen for the multi-vehicle problem. Distributed or decentralized cooperative control is discussed in the context of multi-agent systems. A consensus based cooperative control is studied; such consensus based control problem can be viewed from the algebraic graph theory concepts. The communication structure between the UAVs is represented by the dynamic graph where UAVs are represented by the nodes and the communication links are represented by the edges. The previously designed controller is augmented to account for the group to obtain consensus based on their communication. A theoretical development of the controller for the cooperative group of UAVs is presented and the simulation results for di erent communication topologies are shown. This research also investigates the cases where the communication topology switches to a di erent topology over particular time instants. Lyapunov analysis is performed to show stability in all cases. Another important aspect of this dissertation research is to implement the controller for the case, where perfect or full state information is not available. This necessitates the design of an estimator to estimate the system state. A nonlinear estimator, Extended Kalman Filter (EKF) is rst developed for target tracking with a single UAV. The uncertainties involved with the measurement model and dynamics model are considered as zero mean Gaussian noises with some known covariances. The measurements of the full state of the target are not available and only the range, elevation, and azimuth angle are available from an onboard seeker sensor. A separate EKF is designed to estimate the UAV's own state where the state measurement is available through on-board sensors. The controller computes the three control commands based on the estimated states of target and its own states. Estimation based control laws is also implemented for colored noise measurement uncertainties, and the controller performance is shown with the simulation results. The estimation based control approach is then extended for the cooperative target tracking case. The target information is available to the network and a separate estimator is used to estimate target states. All of the UAVs in the network apply the same control law and the only di erence is that each UAV updates the commands according to their connection. The simulation is performed for both cases of xed and time varying communication topology. Monte Carlo simulation is also performed with di erent sample noises to investigate the performance of the estimator. The proposed technique is shown to be simple and robust to noisy environments.

Download or read book Advanced Control Methods in Marine Robotics Applications written by Fabio Bonsignorio and published by Frontiers Media SA. This book was released on 2021-06-09 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Tracking of Dynamic Targets with Aerial Vehicles Using Quaternion based Techniques written by Hernán Abaunza Gonzalez and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this thesis work is to design control and navigation algorithms for tracking of dynamic ground targets using aerial vehicles. Quaternions, which provide an alternative to the classical representations of aerial vehicle dynamics, have been chosen as a basement to develop robust controllers and agile navigation algorithm, due to their advantages such as the absence of singularities and discontinuities and their mathematical simplicity when handling rotations. The quaternion-based control approaches explored in this thesis range from state feedback, passivity, and energy-based controllers, up to sliding modes, and three-dimensional saturation approaches. Then, autonomous and semi-autonomous navigation strategies for quadrotors were explored. An algorithm has been developed for controlling a quadrotor using gestures from a user wearing an armband. To facilitate the operation of multirotors in adverse scenarios, an aggressive deployment strategy has been proposed where a quadrotor is launched by hand With its motors turned off. Finally, autonomous navigation techniques for tracking dynamic targets have been designed. A trajectory generation algorithm based on differential equations has been introduced to track a land vehicle while describing circles. Finally a distributed path planning algorithm has been developed for a fleet of drones to autonomously track ground targets by solving an online optimization problem.

Download or read book Perception for Control and Control for Perception of Vision based Autonomous Aerial Robots written by Eric Cristofalo and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The mission of this thesis is to develop visual perception and feedback control algorithms for autonomous aerial robots that are equipped with an onboard camera. We introduce light-weight algorithms that parse images from the robot's camera directly into feedback signals for control laws that improve perception quality. We emphasize the co-design, analysis, and implementation of the perception, planning, and control tasks to ensure that the entire autonomy pipeline is suitable for aerial robots with real-world constraints. The methods presented in this thesis further leverage perception for control and control for perception: the former uses perception to inform the robot how to act while the later uses robotic control to improve the robot's perception of the world. Perception in this work refers to the processing of raw sensor measurements and the estimation of state values while control refers to the planning of useful robot motions and control inputs based on these state estimates. The major capability that we enable is a robot's ability to sense this unmeasured scene geometry as well as the three-dimensional (3D) robot pose from images acquired by its onboard camera. Our algorithms specifically enable a UAV with an onboard camera to use control to reconstruct the 3D geometry of its environment in a both sparse sense and a dense sense, estimate its own global pose with respect to the environment, and estimate the relative poses of other UAVs and dynamic objects of interest in the scene. All methods are implemented on real robots with real-world sensory, power, communication, and computation constraints to demonstrate the need for tightly-coupled, fast perception and control in robot autonomy. Depth estimation at specific pixel locations is often considered to be a perception-specific task for a single robot. We instead control the robot to steer a sensor to improve this depth estimation. First, we develop an active perception controller that maneuvers a quadrotor with a downward facing camera according to the gradient of maximum uncertainty reduction for a sparse subset of image features. This allows us to actively build a 3D point cloud representation of the scene quickly and thus enabling fast situational awareness for the aerial robot. Our method reduces uncertainty more quickly than state-of-the-art approaches for approximately an order of magnitude less computation time. Second, we autonomously control the focus mechanism on a camera lens to build metric-scale, dense depth maps that are suitable for robotic localization and navigation. Compared to the depth data from an off-the-shelf RGB-D sensor (Microsoft Kinect), our Depth-from-Focus method recovers the depth for 88% of the pixels with no RGB-D measurements in near-field regime (0.0 - 0.5 meters), making it a suitable complimentary sensor for RGB-D. We demonstrate dense sensing on a ground robot localization application and with AirSim, an advanced aerial robot simulator. We then consider applications where groups of aerial robots with monocular cameras seek to estimate their pose, or position and orientation, in the environment. Examples include formation control, target tracking, drone racing, and pose graph optimization. Here, we employ ideas from control theory to perform the pose estimation. We first propose the tight-coupling of pairwise relative pose estimation with cooperative control methods for distributed formation control using quadrotors with downward facing cameras, target tracking in a heterogenous robot system, and relative pose estimation for competitive drone racing. We experimentally validate all methods with real-time perception and control implementations. Finally, we develop a distributed pose graph optimization method for networks of robots with noisy relative pose measurements. Unlike existing pose graph optimization methods, our method is inspired by control theoretic approaches to distributed formation control. We leverage tools from Lyapunov theory and multi-agent consensus to derive a relative pose estimation algorithm with provable performance guarantees. Our method also reaches consensus 13x faster than a state-of-the-art centralized strategy and reaches solutions that are approximately 6x more accurate than decentralized pose estimation methods. While the computation times between our method and the benchmarch distributed method are similar for small networks, ours outperforms the benchmark by a factor of 100 on networks with large numbers of robots (> 1000). Our approach is easy to implement and fast, making it suitable for a distributed backend in a SLAM application. Our methods will ultimately allow micro aerial vehicles to perform more complicated tasks. Our focus on tightly-coupled perception and control leads to algorithms that are streamlined for real aerial robots with real constraints. These robots will be more flexible for applications including infrastructure inspection, automated farming, and cinematography. Our methods will also enable more robot-to-robot collaboration since we present effective ways to estimate the relative pose between them. Multi-robot systems will be an important part of the robotic future as they are robust to the failure of individual robots and allow complex computation to be distributed amongst the agents. Most of all, our methods allow robots to be more self sufficient by utilizing their onboard camera and by accurately estimating the world's structure. We believe these methods will enable aerial robots to better understand our 3D world.

Download or read book State Estimation for Robotics written by Timothy D. Barfoot and published by Cambridge University Press. This book was released on 2017-07-31 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.

Download or read book Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems 3rd ICAUS 2023 written by Yi Qu and published by Springer Nature. This book was released on with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Monocular Model based 3D Tracking of Rigid Objects written by Vincent Lepetit and published by Now Publishers Inc. This book was released on 2005 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monocular Model-Based 3D Tracking of Rigid Objects reviews the different techniques and approaches that have been developed by industry and research.

Download or read book Robots Drones UAVs and UGVs for Operation and Maintenance written by Diego Galar and published by CRC Press. This book was released on 2020-05-07 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial assets (such as railway lines, roads, pipelines) are usually huge, span long distances, and can be divided into clusters or segments that provide different levels of functionality subject to different loads, degradations and environmental conditions, and their efficient management is necessary. The aim of the book is to give comprehensive understanding about the use of autonomous vehicles (context of robotics) for the utilization of inspection and maintenance activities in industrial asset management in different accessibility and hazard levels. The usability of deploying inspection vehicles in an autonomous manner is explained with the emphasis on integrating the total process. Key Features Aims for solutions for maintenance and inspection problems provided by robotics, drones, unmanned air vehicles and unmanned ground vehicles Discusses integration of autonomous vehicles for inspection and maintenance of industrial assets Covers the industrial approach to inspection needs and presents what is needed from the infrastructure end Presents the requirements for robot designers to design an autonomous inspection and maintenance system Includes practical case studies from industries

Download or read book Unmanned Aircraft Systems written by Ella Atkins and published by John Wiley & Sons. This book was released on 2017-01-17 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: UNMANNED AIRCRAF T SYSTEMS UNMANNED AIRCRAF T SYSTEMS An unmanned aircraft system (UAS), sometimes called a drone, is an aircraft without a human pilot on board ??? instead, the UAS can be controlled by an operator station on the ground or may be autonomous in operation. UAS are capable of addressing a broad range of applications in diverse, complex environments. Traditionally employed in mainly military applications, recent regulatory changes around the world are leading to an explosion of interest and wide-ranging new applications for UAS in civil airspace. Covering the design, development, operation, and mission profiles of unmanned aircraft systems, this single, comprehensive volume forms a complete, stand-alone reference on the topic. The volume integrates with the online Wiley Encyclopedia of Aerospace Engineering, providing many new and updated articles for existing subscribers to that work. The chapters cover the following items: Airframe configurations and design (launch systems, power generation, propulsion) Operations (missions, integration issues, and airspace access) Coordination (multivehicle cooperation and human oversight) With contributions from leading experts, this volume is intended to be a valuable addition, and a useful resource, for aerospace manufacturers and suppliers, governmental and industrial aerospace research establishments, airline and aviation industries, university engineering and science departments, and industry analysts, consultants, and researchers.