Hydrobatic Autonomous Underwater Vehicles (AUVs) are underactuated robots that can perform agile maneuvers in challenging underwater environments with high efficiency in speed and range. The challenge lies in localizing and navigating these AUVs particularly for performing manipulation tasks because common sensors such as GPS become very unreliable underwater due to their poor accuracy. To address this challenge, Visual Odometry (VO) is a viable technique that estimates the position and orientation of a robot by figuring out the movement of a camera and tracking the changes in the associated camera images taken by one or more cameras. VO is a promising solution for underwater localization as it provides information about egomotion utilizing the visual cues in a robot. This research explores the applicability of VO algorithms on hydrobatic AUVs using a simulated underwater dataset obtained in Stonefish, an advanced open-source simulation tool specifically developed for marine robotics. This work focuses on the feasibility of employing two state-of-the-art feature-based VO frameworks, referred to as ORB-SLAM2 and VISO2 respectively since very little research is available for learning-based VO frameworks in underwater environments. The assessment is performed on a baseline underwater dataset captured by cameras of a hydrobatic AUV using the Stonefish simulator in a simulated algae farm, which is one of the target applications of hydrobatic AUVs. A novel software architecture has also been proposed for hydrobatic AUVs, which can be used for integrating VO with other components as a node stack to ensure robust localization. This study further suggests enhancements, including camera calibration and timestamp synchronization, as a future step to optimize VO accuracy and functionality. ORB-SLAM2 performs well in the baseline scenario but is prone to slight drift when turbidity arises in the simulated underwater environment. VISO2 is recommended for such high turbidity scenarios but it fails to estimate the camera motion accurately due to advanced hardware synchronization issues that are prevalent in the dataset as it is highly sensitive to accurate camera calibration and synchronized time stamps. Despite these limitations, the results show immense potential of both ORB-SLAM2 and VISO2 as feature-based VO methods for future deployment in hydrobatic AUVs with ORB-SLAM2 being preferred for overall localization and mapping of hydrobatic AUVs in low turbidity environments that are less prone to drift and VISO2 preferred for high turbidity environments with highly accurate camera calibration and synchronization. / Hydrobatiskt autonomt undervatten Fordon (AUV) är undermanövrerade robotar som kan utföra smidiga manövrar i utmanande undervattensmiljöer med hög effektivitet i hastighet och räckvidd. Utmaningen ligger i att lokalisera och navigering av dessa AUV:er speciellt för att utföra manipulationsuppgifter eftersom vanliga sensorer som GPS blir mycket opålitliga under vattnet på grund av deras dåliga noggrannhet. För att ta itu med detta utmaning, Visual Odometry (VO) är en användbar teknik som uppskattar positionen och orienteringen av en robot genom att räkna ut en kameras rörelse och spåra ändringarna i den tillhörande kameran bilder tagna med en eller flera kameror. VO är en lovande lösning för undervattenslokalisering som den ger information om egomotion med hjälp av de visuella ledtrådarna i en robot. Denna forskning utforskar tillämpbarheten av VO-algoritmer på hydrobatiska AUV:er med hjälp av en simulerad undervattensdatauppsättning erhållen i Stonefish, ett specifikt avancerat simuleringsverktyg med öppen källkod utvecklad för marin robotik. Detta arbete fokuserar på genomförbarheten av att använda två toppmoderna funktionsbaserade VO-ramverk, kallade ORB-SLAM2 respektive VISO2 sedan mycket lite forskning finns tillgänglig för inlärningsbaserade VO-ramverk i undervattensmiljöer. De bedömning utförs på en baslinje undervattensdatauppsättning fångad av kameror från en hydrobatik AUV med hjälp av Stonefish-simulatorn i en simulerad algfarm, vilket är en av målapplikationerna av hydrobatiska AUV:er. En ny mjukvaruarkitektur har också föreslagits för hydrobatiska AUV, som kan användas för att integrera VO med andra komponenter som en nodstack för att säkerställa robust lokalisering. Denna studie föreslår ytterligare förbättringar, inklusive kamerakalibrering och tidsstämpel synkronisering, som ett framtida steg för att optimera VO-noggrannhet och funktionalitet. ORB-SLAM2 presterar bra i baslinjescenariot men är benägen att avvika något när grumlighet uppstår i den simulerade undervattensmiljön. VISO2 rekommenderas för sådana scenarier med hög grumlighet men den misslyckas med att uppskatta kamerarörelsen korrekt på grund av avancerad hårdvarusynkronisering problem som är vanliga i datasetet eftersom det är mycket känsligt för noggrann kamerakalibrering och synkroniserade tidsstämplar. Trots dessa begränsningar visar resultaten en enorm potential för båda ORB-SLAM2 och VISO2 som funktionsbaserade VO-metoder för framtida användning i hydrobatiska AUV:er med ORB-SLAM2 att föredra för övergripande lokalisering och kartläggning av hydrobatiska AUVs i låg grumlighetsmiljöer som är mindre benägna att driva och VISO2 föredras för hög grumlighet miljöer med mycket noggrann kamerakalibrering och synkronisering.

The evolution of technology has led the research into new emerging wearable devices such as the Smart Glasses. This technology provides with new visualization techniques. Augmented Reality is an advanced technology that could significantly ease the execution of much complex operations. Augmented Reality is a combination of both Virtual and Actual Reality, making accessible to the user new tools to safeguard in the transfer of knowledge in several environments and for several processes. This thesis explores the development of an android based image recognition application. The feature point detectors and descriptors are used as they can deal great with the correspondence problems. The selection of best image recognition technique on the smart glasses is chosen based on the time taken to retrieve the results and the amount of power consumed in the process. As the smart glasses are equipped with the limited resources, the selected approach should use low computation on it by making the device operations uninterruptable. The effective and efficient method for detection and recognition of the safety signs from images is selected. The ubiquitous SIFT and SURF feature detectors consume more time and are computationally complex and require very high-level hardware components for processing. The binary descriptors are taken into account as they are light weight and can support low power devices in a much effective style. A comparative analysis is being done on the working of binary descriptors like BRIEF, ORB, AKAZE, FREAK, etc., on the smart glasses based on their performance and the requirements. ORB is the most efficient among the binary descriptors and has been more effective for the smart glasses in terms of time measurements and low power consumption.

Two crucial functionalities of a fully autonomous robotic agent are localization and navigation. The problem of enabling an agent to localize itself in an unknown environment is an extensive and widely studied topic. One of the main areas of this topic focuses on Simultaneous Localization and Mapping (SLAM). Many advancements in this field have been made over the years resulting in robust and accurate localization systems. Navigation progress has also improved substantially throughout the years resulting in efficient path planning algorithms and effective exploration strategies. Although an abundance of research exists on these two topics, less so exists about the combination of the two and their effect on each other. Therefore, the aim of this thesis was to integrate two state-of-the-art components from each respective area of research into a functioning system. This was done with the aim of studying the interconnectivity between these components while also documenting the integration process and identifying important considerations for similar future endeavours. Evaluations of the system showed that it performed with surprisingly good accuracy although it was severely lacking in robustness. Integration efforts showed good promise; however, it is clear that the two fields are heavily linked and need to be considered in a mutual context when it comes to a complete integrated system. / Förmågor som lokalisering och navigering är inom robotik förutsättande för att kunna möjliggöra en fullt autonom agent. Att för en agent kunna lokalisera sig i en okänd miljö är ett omfattande och brett studerat ämne, och ett huvudfokus inom ämnet är Simultaneous Localization and Mapping (SLAM) som avser lokalisering som sker parallellt med en aktiv kartläggning av omgivningen. Stora framsteg har gjorts inom detta område genom åren, vilket har resulterat i robusta och exakta system för robotlokalisering. Motsvarande framsteg inom robotnavigering har dessutom möjliggjort effektiva algoritmer och strategier för path planning och autonom utforskning. Trots den stora mängd forskning som existerar inom ämnena lokalisering och navigation var för sig, är samspelet mellan de två områdena samt möjligheten att sammankoppla de två aspekterna mindre studerat. I syfte att undersöka detta var målet med detta examensarbete således att integrera två toppmoderna system från de respektive områdena till ett sammankopplat system. Utöver att förmågorna och prestandan hos det integrerade systemet kunde studeras, genomfördes studien med avsikten att möjliggöra dokumentering av integrationsprocessen samt att viktiga insikter kring integrationen kunde identifieras i syfte att främja framtida studier inom samspelet mellan områdena lokalisering och navigation. Utvärderingar av det integrerade systemet påvisade en högre nivå av noggrannhet än förväntat, men fann en markant avsaknad av robusthet. Resultaten från integrationsarbetet anses lovande, och belyser framförallt att finns ett starkt samband mellan de två områdena samt att de bör beaktas i ett gemensamt kontext när de avses användas i ett komplett integrerat system.

Offloading SLAM to the edge/cloud is now becoming an attractive option to greatly decrease device energy usage. The new SLAM solution involves compressing image data on the device before transmission, allowing a further decrease in the network bandwidth when performing SLAM at the edge/cloud. However, lossy compression affects the quality of images, making image features harder to detect and track during visual SLAM operation, impacting localization accuracy. Current visual SLAM implementations assume that images are non-compressed since SLAM is traditionally executed onboard the device to which a camera is directly connected. This thesis work explores the impact of image compression on the localization accuracy of ORBSLAM3, a representative visual SLAM system, and in what way the ORBSLAM3’s modules for feature detection and matching are affected. Methods are proposed that adapt the image bitrates based on the number of features detected and enhance the image brightness for low-light conditions, plus optimizing the internal parameters in SLAM, to improve the robustness of the overall system to image compression. The experiment results show the detailed influence of the impact brought by compression on ORB-SLAM3 and prove the effectiveness of our methods. Also, integrating these methods yields synergistic improvements. While this thesis work primarily addresses the SLAM system’s front-end, future work can target back-end modifications. / Att avlasta SLAM till kanten/molnet blir nu ett attraktivt alternativ för att markant minska enheters energiförbrukning. Den nya SLAM-lösningen innebär att bilddata komprimeras på enheten innan den överförs, vilket möjliggör ytterligare minskning av nätverksbandbredden vid genomförandet av SLAM vid kanten/molnet. Men förlustkomprimering påverkar bildernas kvalitet och gör det svårare att upptäcka och följa bildfunktioner under visuell SLAM-drift, vilket påverkar lokaliseringsnoggrannheten. Nuvarande implementationer av visuell SLAM förutsätter att bilderna inte är komprimerade eftersom SLAM traditionellt utförs ombord på den enhet till vilken en kamera är direkt ansluten. Denna avhandling utforskar effekten av bildkomprimering på lokaliseringsnoggrannheten hos ORB-SLAM3, en representativ visuell SLAM-system, och på vilket sätt ORB-SLAM3: s moduler för funktionssökning och matchning påverkas. Metoder föreslås som anpassar bildbitarna baserat på antalet detekterade funktioner och förbättrar bildens ljusstyrka för svagt ljusförhållanden, samt optimerar de interna parametrarna i SLAM för att öka hela systemets robusthet mot bildkomprimering. Experimentresultaten visar den detaljerade påverkan som kompression har på ORB-SLAM3 och bevisar effektiviteten hos våra metoder. Dessutom ger integration av dessa metoder synergi och förbättringar. Även om denna avhandling primärt fokuserar på SLAM-systemets framsida, kan framtida arbete rikta sig mot bakkantsmodifieringar.

Ingeniero Civil Eléctrico / Este trabajo consiste en la generación de un set de datos con un respectivo ground truth (medición más confiable) y el uso de los algoritmos ORB-SLAM (Orientated FAST and Rotated BRIEF (Binary Robust Independent Elementary Features) Simultaneous Location And Mapping) y LOAM (Lidar Odometry And Mapping) a modo de entender de mejor forma el problema de SLAM (localización y mapeo simultaneo) y comparar los resultados obtenidos con el ground truth. A modo de entender de mejor forma el set de datos generado, la funcionalidad de los diferentes sensores es explicada. Los sensores utilizados para generar los datos son LIDAR, cámara estéreo y GPS. Este trabajo posee dos mayores etapas, en primer lugar, el GPS es estudiado para establecer las diferentes formas de extraer los datos desde el dispositivo. Una forma es generar un nodo de ROS que mediante comunicación de Bluetooth otorga un mensaje que puede ser leído. Otra forma es presionar tres veces el botón de encendido del GPS, lo que inicia el almacenamiento de los datos en la tarjeta SD. Mientras el primer método entrega mayor cantidad de información, es menos confiable, existiendo la posibilidad de guardar mensajes vacios o perdida de ciertos datos, afectando la tasa de muestreo. Finalmente una combinación de ambos métodos es implementada. Un set de datos de prueba es generado cerca de la Universidad De Chile, para probar que los datos están siendo almacenados correctamente. En el test se concluye que a modo de obtener mejor resultado con el GPS es necesario tomar los datos en zonas con baja cantidad de edificios. Finalmente con los datos y el ground truth el Error Absoluto de la Trayectoria (ATE) es calculado como método de comparación de ambas trayectorias generadas con los algoritmos mencionados. El ATE s la cantidad de energía necesaria para transformar la trayectoria estimada en el ground truth. Dadas ciertas limitaciones en la extracción de los datos estimados, la comparación se realizo entre dos set de datos de prueba, con pequeña cantidad de loops en el camino recorrido. En esta situación los resultados dados por LOAM son mejores que los obtenidos con ORB.SLAM. Pero en un ambiente con mayor cantidad de loops y una trayectoria más larga ORB-SLAM entregaría mejores resultados. ABSTRACT This work consists of the generation of a data-set with ground truth and the use of ORB-SLAM (Orientated FAST and Rotated BRIEF (Binary Robust Independent Elementary Features) Simultaneous Location And Mapping) and LOAM (Lidar Odometry And Mapping) algorithms as a way to better understand SLAM and to compare the ground truth and the data-set generated. To fully understand the data-set generated, the functionality of the different sensors is explained. The sensors used to generate the data-set are LIDAR, Stereo Camera and a GPS. This work is divided into two stages, in the first place the GPS is studied to establish the different ways to extract the data from it. One way is to generate a ROS node that through Bluetooth communication generates a message which is published. The other way is to press three times the button of the GPS to store the data in the GPS micro SD memory. While the first method is capable of store more data per second, it is less reliable, existing the possibility of store an empty message or simply the loss of data in the process. In the end, a combination of the two methods is implemented, modifying the bag file with the data stored in the micro SD. A test-data is generated near the University Of Chile, to prove that the bag file (a type of file that can contain any kind of information such as images, video or text, between others) is correctly generated. In these tests, it was concluded that to obtain better performance of the GPS therefore, obtain a better ground truth, it was necessary to generate the data in a zone with a low quantity of high buildings. Finally with the data-set and the ground truth the Absolute Trajectory Error (ATE) is used as a method to compare the trajectories. The ATE is the amount of energy that would require to transform the estimated trajectory on the ground truth. Since certain limitations of the extraction of the estimated path, the comparison was made between two small data-set which counted with low quantity of closed loops. Therefore the LOAM algorithm shows better results in this trajectory. The ORB-SLAM algorithm shows better results in data-sets with a high quantity of loops in the path.

Webs are fundamental to the ecology of Nephila clavipes, the golden orb-weaver spider, because they serve as sites for prey capture, reproduction, competition, predation, and parasitism. In addition to the presence of the female N. clavipes, males and kleptoparasites reside on the web in varying numbers. Webs are also found in clusters with conspecific females. Web site selection and length of web tenure is a behavioral decision vital to the spider’s fitness and the ecology of her species. I conducted a field census to quantify these factors and analyze their influence on web tenure, compare web ecology between a forest and non-forest habitat, as well as explain the significance of N. clavipes’ web as central to many interactions. Web tenure, as well, was influenced differently by the factors between both environments. In the forest habitat, increase in prey capture rate decreased web tenure and inclusion in cluster increased web tenure. In the non-forest habitat, only increase in spider size was related to increased web tenure. There were significant differences between the two habitats in the sizes of the female spider and quantity of males and kleptoparasites. Results also showed that spider size influenced quantity of males and web diameter influenced quantity of kleptoparasites. Explanation of these results can be attributed to the complex relationships among the variables and the consequences of living in habitats impacted by human occupation.

Sharing locally stored media files like music, videos and pictures has not been user-friendly for a long time now. Nowadays people, when they know where the media is stored, have to use the complicated network shares or external storage solutions like USB sticks, hard drives or even CD/DVDs to share media across different users. When the users do not know where the media is stored, they have to use Internet-based peer- to-peer applications like LimeWire, KaZaa or the Gnutella-network, which requires searching and downloading the media first, before being able to actually make use of it. But what if you do not have an Internet connection, do not want to mess around with external storage solutions nor want to wait while downloading or copying from a (network) device, but still want to make use of the media stored on another computer system? Indeed, nowadays there is not any easy solution that provides a very user-friendly, fast and responsive, flexible and stable solution for this. This problem brought us to our research question: “Is there a very easy solution for sharing or watching media throughout the local network?” After some research we stumbled upon some state-of-the-art technologies, which came very close to what we wanted to achieve, however, still having quite some drawbacks, not suitable as a solution for the problem mentioned above. We decided to innovate and tried to find a solution without any drawbacks while still being very user-friendly. We achieved quite good research results showing that: • Using a client-only network was the most efficient and flexible way to provide a stable network structure; • Java was the best programming language to provide a cross-platform application; • For compatibility with the media sharing itself an object-oriented based indexing storage structure, like db4o, yielded the best flexibility and speed in comparison with SQL or other technologies; • The streaming of the media could be achieved best by making use of Java VLC libraries. The user-friendliness of the demo application that we created was also very good, only a few clicks are sufficient to share your media across the network, no need to bother about user rights and so on. We can conclude that our research can be the base of a very successful innovative media sharing system and strongly believe, with some more adjustments in the future, that it has potential to become a very popular application along the media sharing industry.

Webs are fundamental to the ecology of Nephila clavipes, the golden orb-weaver spider, because they serve as sites for prey capture, reproduction, competition, predation, and parasitism. In addition to the presence of the female N. clavipes, males and kleptoparasites reside on the web in varying numbers. Webs are also found in clusters with conspecific females. Web site selection and length of web tenure is a behavioral decision vital to the spider’s fitness and the ecology of her species. I conducted a field census to quantify these factors and analyze their influence on web tenure, compare web ecology between a forest and non-forest habitat, as well as explain the significance of N. clavipes’ web as central to many interactions. Web tenure, as well, was influenced differently by the factors between both environments. In the forest habitat, increase in prey capture rate decreased web tenure and inclusion in cluster increased web tenure. In the non-forest habitat, only increase in spider size was related to increased web tenure. There were significant differences between the two habitats in the sizes of the female spider and quantity of males and kleptoparasites. Results also showed that spider size influenced quantity of males and web diameter influenced quantity of kleptoparasites. Explanation of these results can be attributed to the complex relationships among the variables and the consequences of living in habitats impacted by human occupation.

Thesis (M. Sc.)--Carleton University, 2004. / Includes bibliographical references (p. 95-97). Also available in electronic format on the Internet.

Visual Simultaneous Localisation And Mapping is a useful tool forcreating 3D environments with feature points. These visual systemscould be very valuable in autonomous vehicles to improve the localisation.Cameras being a fairly cheap sensor with the capabilityto gather a large amount of data. More efficient algorithms are stillneeded to better interpret the most valuable information. This paperanalyses how much a feature based map can be reduced without losingsignificant accuracy during localising. Semantic segmentation created by a deep neural network is used toclassify the features used to create the map, the map is reduced by removingcertain classes. The results show that feature based maps cansignificantly be reduced without losing accuracy. The use of classesresulted in promising results, large amounts of feature were removedbut the system could still localise accurately. Removing some classesgave the same results or even better in certain weather conditionscompared to localisation with a full-scale map.