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Author: Michael Kent Kaiser Publisher: ISBN: Category : Languages : en Pages :
Book Description
The dissertation will focus on three problems of interest: 1) vehicle state estimation and control using a homography-based daisy-chaining approach; 2) Lyapunov-based nonlinear state estimation and range identification using a pinhole camera; 3) robust aerial vehicle control in the presence of structured and unstructured uncertainties.
Author: Michael Kent Kaiser Publisher: ISBN: Category : Languages : en Pages :
Book Description
The dissertation will focus on three problems of interest: 1) vehicle state estimation and control using a homography-based daisy-chaining approach; 2) Lyapunov-based nonlinear state estimation and range identification using a pinhole camera; 3) robust aerial vehicle control in the presence of structured and unstructured uncertainties.
Author: Publisher: ISBN: Category : Cameras Languages : en Pages : 41
Book Description
In the tracking of a moving ground target by small unmanned air vehicle (UAV) via camera vision, the target position and motion cannot be measured directly. Two different types of filters were assessed for their ability to estimate target motion, namely target velocity, directional heading on flat ground and distance from the UAV to target. The first filter is a nonlinear deterministic filter with stability guarantee. The second filter is based on nonlinear Kalman Filter technique. The application and performance of these two filters are presented, for simulated vision based target tracking.
Author: Chingiz Hajiyev Publisher: Springer ISBN: 3319164171 Category : Technology & Engineering Languages : en Pages : 239
Book Description
This book discusses state estimation and control procedures for a low-cost unmanned aerial vehicle (UAV). The authors consider the use of robust adaptive Kalman filter algorithms and demonstrate their advantages over the optimal Kalman filter in the context of the difficult and varied environments in which UAVs may be employed. Fault detection and isolation (FDI) and data fusion for UAV air-data systems are also investigated, and control algorithms, including the classical, optimal, and fuzzy controllers, are given for the UAV. The performance of different control methods is investigated and the results compared. State Estimation and Control of Low-Cost Unmanned Aerial Vehicles covers all the important issues for designing a guidance, navigation and control (GNC) system of a low-cost UAV. It proposes significant new approaches that can be exploited by GNC system designers in the future and also reviews the current literature. The state estimation, control and FDI methods are illustrated by examples and MATLAB® simulations. State Estimation and Control of Low-Cost Unmanned Aerial Vehicles will be of interest to both researchers in academia and professional engineers in the aerospace industry. Graduate students may also find it useful, and some sections are suitable for an undergraduate readership.
Author: Aleksander Nawrat Publisher: Springer ISBN: 3319003690 Category : Technology & Engineering Languages : en Pages : 348
Book Description
This monograph is motivated by a significant number of vision based algorithms for Unmanned Aerial Vehicles (UAV) that were developed during research and development projects. Vision information is utilized in various applications like visual surveillance, aim systems, recognition systems, collision-avoidance systems and navigation. This book presents practical applications, examples and recent challenges in these mentioned application fields. The aim of the book is to create a valuable source of information for researchers and constructors of solutions utilizing vision from UAV. Scientists, researchers and graduate students involved in computer vision, image processing, data fusion, control algorithms, mechanics, data mining, navigation and IC can find many valuable, useful and practical suggestions and solutions. The latest challenges for vision based systems are also presented.
Author: Yi Ma Publisher: Springer Science & Business Media ISBN: 0387217797 Category : Computers Languages : en Pages : 542
Book Description
This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.
Author: Julien Francois Li-Chee-Ming Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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%.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Abstract : Quadcopters have been developed with controls providing good maneuverability, simple mechanics, and the ability to hover, take-off and land vertically with precision. Due to their small size, they can get close to targets of interest and furthermore stay undetected at lower heights. The main drawbacks of a quadcopter are its high-power consumption and payload restriction, due to which, the number of onboard sensors is constrained. To overcome this limitation, vision-based localization techniques and remote control for the quadcopter are essential areas of current research. The core objective of this research is to develop a closed loop feedback system between an Unmanned Aerial Vehicle (UAV) and a mobile ground vehicle. With this closed loop system, the moving ground vehicle aims to navigate the UAV remotely. The ground vehicle uses a pure pursuit algorithm to traverse a pre-defined path. A Proportional-Integral-Derivative (PID) controller is actualized for position control and attitude stabilization of the UAV. The issue of tracking and 3D pose-estimation of the UAV in light of vision sensing is explored. An estimator to track the states of the UAV, utilizing the images obtained from a single camera mounted on the ground vehicle is developed. This estimator coupled with a Kalman filter determines the UAV's three dimensional position. The relative position of the UAV with the moving ground vehicle and the control output from a joint centralized PD controller is used to navigate the UAV and follow the motion of the ground vehicle in closed loop to avoid time delays. This closed loop system is simulated in MATLAB and Simulink to validate the proposed control and estimation approach. The results obtained validate the control architecture proposed to attain closed loop feedback between the UAV and the mobile ground vehicle.
Author: Michael Kent Kaiser
Author: Michael Kent Kaiser
Author: 
Author: Chingiz Hajiyev
Author: Felipe Gonzalez Toro
Author: Aleksander Nawrat
Author: Yi Ma
Author: Julien Francois Li-Chee-Ming
Author: Mingfeng Zhang
Author: 
Author: Felipe Gonzalez Toro