Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops

This paper reviews the use of modern 3D image-based and Light Detection and Ranging (LiDAR) methods of surface reconstruction techniques for high fidelity surveys of small rock outcrops to highlight their potential within structural geology and landscape protection. LiDAR and Structure from Motion (SfM) software provide useful opportunities for rock outcrops mapping and 3D model creation. The accuracy of these surface reconstructions is crucial for quantitative structural analysis. However, these technologies require either a costly data acquisition device (Terrestrial LiDAR) or specialized image processing software (SfM). Recent developments in augmented reality and smartphone technologies, such as increased processing capacity and higher resolution of cameras, may offer a simple and inexpensive alternative for 3D surface reconstruction. Therefore, the aim of the paper is to show the possibilities of using smartphone applications for model creation and to determine their accuracy for rock outcrop mapping.O

The GOGIRA System: An Innovative Method for Landslides Digital Mapping

Landslide mapping techniques have had many improvements in recent decades, the main field of development has been on traditional cartographic techniques and to a lesser extent on indirect numerical cartography. As for Direct Numerical Cartography (DNC), only a few improvements have been made due to the complexity and economic cost of the new technologies. To meet this lack in DNC techniques GOGIRA (Ground Operative-system for GIS Input Remote-data Acquisition), a new system following the GIS (Geographic Information System) scheme, was developed. It is a suite of hardware and software tools, algorithms, and procedures for easier and cheaper DNC. Initial tests conducted on the Quincinetto landslide system (north-western Italy) demonstrated good results in terms of morphometric coherence and precision. A geomorphological map made with GOGIRA was compared with a highly detailed geomorphological map developed with modern tested methods. In conclusion GOGIRA proved to be a valid system for geomorphological DNC when applied to a complex landslide system, considering the early stage of developing results for linear and point mapping was excellent, as for polygonal elements more studies must be conducted to improve accuracy and precision

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Estimating the orientation of a game controller from inertial and magnetic measurements

L’estimation de l’orientation d’un corps rigide en mouvement dans l’espace joue un rôle indispensable dans les technologies de navigation, par exemple, les systèmes militaires de missiles, les avions civils, les systèmes de navigation chirurgicale, la cartographie faite par des robots, les véhicules autonomes et les contrôleurs de jeux. Cette technique est maintenant utilisée dans certaines applications qui nous touchent directement, notamment dans les contrôleurs de jeux tels que la Wii-mote. Dans cette veine, la recherche présentée ici porte sur l’estimation de l’orientation d’un corps rigide à partir des mesures de capteurs inertiels et magnétiques peu coûteux. Comme les capteurs inertiels permettent de mesurer les dérivées temporelles de l’orientation, il est naturel de commencer par l’estimation de la vitesse angulaire. Par conséquent, nous présentons d’abord une nouvelle façon de déterminer la vitesse angulaire d’un corps rigide à partir d’accéléromètres. Ensuite, afin d’estimer l’orientation, nous proposons une nouvelle méthode d’estimation de l’orientation d’un corps rigide dans le plan vertical à partir des mesures d’accéléromètres, en discernant ses composantes inertielle et gravitationnelle. Mais, ce n’est sûrement pas suffisant d’estimer l’orientation dans le plan vertical, parce que la plupart des applications se produisent dans l’espace tridimensionnel. Pour estimer les rotations dans l’espace, nous présentons d’abord la conception d’un contrôleur de jeu, dans lequel tous les capteurs nécessaires sont installés. Ensuite, ces capteurs sont étalonnés pour déterminer leurs facteurs d’échelle et leurs zéros, de manière à améliorer leurs exactitudes. Ensuite, nous développons une nouvelle méthode d’estimation de l’orientation d’un corps rigide se déplaçant dans l’espace, encore en discernant les composantes gravitationnelle et inertielle des accélérations. Finalement, pour imiter le contrôleur de jeu Wii, nous créons une interface usager simple de sorte qu’une représentation virtuelle du contrôleur de jeu puisse suivre chaque mouvement du contrôleur de jeu conçu (réalité virtuelle). L’interface usager conçue montre que l’algorithme proposé est suffisamment précis pour donner à l’usager un contrôle fidèle de l’orientation du contrôleur de jeu virtuel.Estimating the orientation of a rigid-body moving in space is an indispensable component of navigation technology, e.g., military missile systems, civil aircrafts, surgical navigation systems, robot mapping, autonomous vehicles and game controllers. It has now come directly into some aspects of our lives, notoriously in game controllers, such as the Wiimote. In this vein, this research focuses on the development of new algorithms to estimate the rigid-body orientation from common inexpensive inertial and magnetic sensors. As inertial sensors measure the time derivatives of the orientation, it is natural to start with the estimation of the angular velocity. More precisely, we present a novel way of determining the angular velocity of a rigid body from accelerometer measurements. This method finds application in crashworthiness and motion analysis in sports, for example, where impacts forbid the use of mechanical gyroscopes. Secondly, in an attempt to estimate the orientation in a simplified setting, we propose a novel method of estimating the orientation of a rigid body in the vertical plane from point-acceleration measurements, by discerning its gravitational and inertial components. Thirdly, it is surely not enough to estimate the orientation in the vertical plane, because most applications take place in three dimensions. For estimating rotations in space, we first present the game controller design, in which all necessary sensors are installed. Then, these sensors are calibrated to determine their scale factors and offsets so as to improve their performances. Thence, we develop a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, also by discerning the gravitational and inertial components of the acceleration. Finally, in order to imitate the game controller Wii, we create a simple user interface in which a virtual representative of the game controller follows every orientation of the true game controller (virtual reality). The user interface shows that the proposed algorithm is sufficiently accurate to give the user a transparent control of the orientation of the virtual game controller

Improving Real-World Performance of Vision Aided Navigation in a Flight Environment

The motivation of this research is to fuse information from an airborne imaging sensor with information extracted from satellite imagery in order to provide accurate position when GPS is unavailable for an extended duration. A corpus of existing geo-referenced satellite imagery is used to create a key point database. A novel algorithm for recovering coarse pose using by comparing key points extracted from the airborne imagery to the reference database is developed. This coarse position is used to bootstrap a local-area geo-registration algorithm, which provides GPS-level position estimates. This research derives optimizations for existing local-area methods for operation in flight environments

Scaling Machine Learning Systems using Domain Adaptation

Machine-learned components, particularly those trained using deep learning methods, are becoming integral parts of modern intelligent systems, with applications including computer vision, speech processing, natural language processing and human activity recognition. As these machine learning (ML) systems scale to real-world settings, they will encounter scenarios where the distribution of the data in the real-world (i.e., the target domain) is different from the data on which they were trained (i.e., the source domain). This phenomenon, known as domain shift, can significantly degrade the performance of ML systems in new deployment scenarios. In this thesis, we study the impact of domain shift caused by variations in system hardware, software and user preferences on the performance of ML systems. After quantifying the performance degradation of ML models in target domains due to the various types of domain shift, we propose unsupervised domain adaptation (uDA) algorithms that leverage unlabeled data collected in the target domain to improve the performance of the ML model. At its core, this thesis argues for the need to develop uDA solutions while adhering to practical scenarios in which ML systems will scale. More specifically, we consider four scenarios: (i) opaque ML systems, wherein parameters of the source prediction model are not made accessible in the target domain, (ii) transparent ML systems, wherein source model parameters are accessible and can be modified in the target domain, (iii) ML systems where source and target domains do not have identical label spaces, and (iv) distributed ML systems, wherein the source and target domains are geographically distributed, their datasets are private and cannot be exchanged using adaptation. We study the unique challenges and constraints of each scenario and propose novel uDA algorithms that outperform state-of-the-art baselines