Obstacle Avoidance Methods in UAVs

We contributed a method for avoiding obstacles using monocular vision as the only sensor in UAV (Unmaned Aerial vehicle). The vision based ROS (Robotic operating system) node detects the known obstacles in front of the UAV. Unknown obstacles can be taken care of by adding he information of all the obstacles seen in the scene to a map. The distance to obstacle in this research is calculated by just increasing size of the obstacle in front of the UAV. The image processing libraries were used from OpenCV to do thresholding, noise removal and contours detection. This research also tests and evaluate the path planning of UAV using MoveIt architecture, and evaluates the different results obtained.Hence we show the effectiveness of the monocular vision and size as a constraint algorithm in UAVs to detect and avoid frontal obstacles

Analisis orientado a objetos de imágenes de teledetección para cartografia forestal : bases conceptuales y un metodo de segmentacion para obtener una particion inicial para la clasificacion = Object-oriented analysis of remote sensing images for land cover mapping : Conceptual foundations and a segmentation method to derive a baseline partition for classification

El enfoque comúnmente usado para analizar las imágenes de satélite con fines cartográficos da lugar a resultados insatisfactorios debido principalmente a que únicamente utiliza los patrones espectrales de los píxeles, ignorando casi por completo la estructura espacial de la imagen. Además, la equiparación de las clases de cubierta a tipos de materiales homogéneos permite que cualquier parte arbitrariamente delimitada dentro de una tesela del mapa siga siendo un referente del concepto definido por su etiqueta. Esta posibilidad es incongruente con el modelo jerárquico del paisaje cada vez más aceptado en Ecología del Paisaje, que asume que la homogeneidad depende de la escala de observación y en cualquier caso es más semántica que biofísica, y que por tanto los paisajes son intrínsecamente heterogéneos y están compuestos de unidades (patches) que funcionan simultáneamente como un todo diferente de lo que les rodea y como partes de un todo mayor. Por tanto se hace necesario un nuevo enfoque (orientado a objetos) que sea compatible con este modelo y en el que las unidades básicas del análisis sean delimitadas de acuerdo a la variación espacial del fenómeno estudiado. Esta tesis pretende contribuir a este cambio de paradigma en teledetección, y sus objetivos concretos son: 1.- Poner de relieve las deficiencias del enfoque tradicionalmente empleado en la clasificación de imágenes de satélite. 2.- Sentar las bases conceptuales de un enfoque alternativo basado en zonas básicas clasificables como objetos. 3.- Desarrollar e implementar una versión demostrativa de un método automático que convierte una imagen multiespectral en una capa vectorial formada por esas zonas. La estrategia que se propone es producir, basándose en la estructura espacial de las imágenes, una partición de estas en la que cada región puede considerarse relativamente homogénea y diferente de sus vecinas y que además supera (aunque no por mucho) el tamaño de la unidad mínima cartografiable. Cada región se asume corresponde a un rodal que tras la clasificación será agregado junto a otros rodales vecinos en una región mayor que en conjunto pueda verse como una instancia de un cierto tipo de objetos que más tarde son representados en el mapa mediante teselas de una clase particular

Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques

In this paper, the notion of risk analysis within 3D scenes using vision based techniques is introduced. In particular the problem of risk estimation of indoor environments at the scene and object level is considered, with applications in domestic robots and smart homes. To this end, the proposed Risk Estimation Framework is described, which provides a quantified risk score for a given scene. This methodology is extended with the introduction of a novel robust kernel for 3D shape descriptors such as 3D HOG and SIFT3D, which aims to reduce the effects of outliers in the proposed risk recognition methodology. The Physics Behaviour Feature (PBF) is presented, which uses an object's angular velocity obtained using Newtonian physics simulation as a descriptor. Furthermore, an extension of boosting techniques for learning is suggested in the form of the novel Complex and Hyper-Complex Adaboost, which greatly increase the computation efficiency of the original technique. In order to evaluate the proposed robust descriptors an enriched version of the 3D Risk Scenes (3DRS) dataset with extra objects, scenes and meta-data was utilised. A comparative study was conducted demonstrating that the suggested approach outperforms current state-of-the-art descriptors

Low-cost autonomous car level 2: Design and implementation for conventional vehicles

Modern cars are equipped with autonomous systems to assist the driver and improve driving experience. Driving assist system (DAS) is one of the most significant components of a self-driving vehicle (SDV), used to overcome non-autonomous driving challenges. However, most conventional cars are not equipped with DAS, and high-cost systems are required to equip these vehicles with DAS. Moreover, the design of DAS is very complex outside of the industry while it requires going through the Electronic Control Unit (ECU), which has a high level of security. Therefore, a basic system needs be installed in conventional cars which makes driving more efficient in terms of driver assistance. In this paper, an intelligent DAS is presented for real-time prediction of steering angle using deep learning (DL) and raw dataset collected from a real environment. Furthermore, an object detection model is deployed to assist and warn the driver of various types of objects along with corresponding distance measurement based on DL. Outputs from DL models are fed into the steering control system, which has Electronic Power Steering (EPS). The steering angle is measured in real time using an angle sensor and is posted back to the steering control system to make automated adjustments accordingly. Real-time tests are conducted on a 2009 Toyota Corolla equipped with a digital camera to capture live video stream, Controller Area Network (CAN-BUS) messages, and a steering angle sensor. The performance evaluation of the proposed system indicates intelligent steering control and driver assistance when evaluated in a real-time environment

Underwater Pipeline Leakage Detection Using Vision Based Techniques: Semi-AUV (SAUV) Approach

This thesis intends to convert a Remote Operated Vehicle (ROV) to a Semi-Autonomous Underwater Vehicle (SAUV) using a vision-based control system. The SAUV was used for automatic underwater gas pipeline tracking and leakage detection. the leakages in the pipeline using Computer Vision. The SAUV was designed to operate both manually and automatically in underwater conditions. The proposed SAUV has 6 thrusters to achieve 4 degrees of freedom controlled by the controller unit and powered by LiPo battery packs. Our underwater vehicle is equipped with sensors providing continuous feedback signals to automatically control the vehicle to track predefined trajectories. The SAUV can be self-stabilized as the center of gravity and center of buoyancy of the vehicle is positioned in such a way in the predefined plan. The SAUV captures images to perform line tracking along with the pipeline and gas bubble images during its mission. The multi-core umbilical cable is used here for the video signal, the feedback signal, and battery charging lines. This will be used only for development and test purposes and will be removed during autonomous missions. For performing all operations, various control schemes such as computer vision algorithm for object detection using python programming, OpenCV, Hough Transform Theory, etc. are applied. The proposed SAUV is expected to pave the way for the development of advanced underwater oil and gas pipeline industrial applications by ocean scientists

Energy-Efficiency of Conveyor Belts in Raw Materials Industry

This book focuses on research related to the energy efficiency of conveyor transportation. The solutions presented in the Special Issue have an impact on optimizing, and thus reducing, the costs of energy consumption by belt conveyors. This is due, inter alia, to the use of better materials for conveyor belts, which reduce its rolling resistance and noise, and improve its ability to adsorb the impact energy from the material falling on the belt. The use of mobile robots designed to detect defects in the conveyor's components makes the conveyor operation safer, and means that the conveyor works for longer and there are no unplanned stops due to damage

Sea Ice Mapping in Labrador Coast with Sentinel-1 Synthetic Aperture Radar Imagery

Sea ice mapping is crucial to Canadian coast, including marine transportation, environmental protection, resource management, disaster and emergency management, especially under current background of climate change. Canadian RADARSAT-2, like other synthetic aperture radar (SAR) sensors, is an essential source for current sea ice mapping in Canada, However, its limited revisiting makes daily ice chart generation challenging. The RADARSAT Constellation project is expected to be launched in 2018, the gap of data availability is expected to be filled with imagery from multiple sources. Sentinel-1, launched by European Space Agency (ESA) in late 2014, is an alternative source for sea ice mapping with comparable capability of RADARSAT-2 in wide swath mode. The main objective of this study is to examine the performance of Sentinel-1 imagery in sea ice mapping with a semi-automated image segmentation workflow. The methodology consists of two main steps. First, the most significant features in sea ice interpretation were determined using a random forest feature selection method. Second, an unsupervised graph-cut image segmentation is performed. The workflow was tested on 15 dual-polarized Sentinel-1A Extra Wide (EW) scenes in Labrador coast from December, 2015 to June, 2016, and the results were evaluated on the accuracy of water segmentation. The study found that: 1) GLCM features are effective in distinguishing different ice classes and 6 most important features were selected; 2) the proposed semi-automated workflow is able to segment Sentinel-1 imagery into 3 to 8 classes for water identification; and 3) generally Sentinel-1 imagery has similar responses from first-year ice compared with previous sensors, but with a different noise pattern in cross-polarized bands; and the overall accuracy of water identification reached close to 95%

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing