Unmanned ground vehicles (UGVs) have been widely used in many areas such as agriculture, mining, construction and military applications. This results from the fact that UGVs can not only be easily built and controlled, but also be featured with high mobility and handling hazardous situations in complex environments. Among the competences of UGVs, autonomous navigation is one of the most challenging problems. This is because that the success in achieving autonomous navigation depends on four factors: Perception, localization, cognition, and proper motion controller. In this thesis, we introduce the realization of autonomous navigation for a two-wheeled differential ground robot under the robot operating system (ROS) environment from both the simulation and experimental perspectives. In Chapter 2, the simulation work is discussed. Firstly, the robot model is described in the unified robot description format (URDF)-based form and the working environment for the robot is simulated. Then we use the \textit{gmapping} package which is one of the packages integrating simultaneous localization and mapping (SLAM) algorithm to build the map of the working environment. In addition, ROS packages including \textit{tf}, \textit{move\_base}, \textit{amcl}, etc., are used to realize the autonomous navigation. Finally, simulation results show the feasibility and effectiveness of the autonomous navigation system for the two-wheeled UGV with the ability to avoid collisions with obstacles. In Chapter 3, we introduce the experimental studies of implementing autonomous navigation for a two-wheeled UGV. The necessary hardware peripherals on the UGV to achieve autonomous navigation are given. The process of implementation in the experiment is similar to that in simulation, however, calibration of several devices is necessary to adapt the scenario in a practical environment. Additionally, a proportional-integral-derivative (PID) controller for the robot base is used to handle the external noise during the experiment. The experimental results demonstrate the success in the implementation of autonomous navigation for the UGV in practice. / Graduate

abstract: In this thesis, the synergy between millimeter-wave (mmWave) imaging and wireless communications is used to achieve high accuracy user localization and mapping (SLAM) mobile users in an uncharted environment. Such capability is enabled by taking advantage of the high-resolution image of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects that mmWave imaging provides, and by utilizing angle of arrival (AoA) and time of arrival (ToA) estimators from communications. The motivations of this work are as follows: first, enable accurate SLAM from a single viewpoint i.e., using only one antenna array at the base station without any prior knowledge of the environment. The second motivation is the ability to localize in NLoS-only scenarios where the user signal may experience more than one reflection until it reaches the base station. As such, this proposed work will not make any assumptions on what region the user is and will use mmWave imaging techniques that will work for both near and far field region of the base station and account for the scattering properties of mmWave. Similarly, a near field signal model is developed to correctly estimate the AoA regardless of the user location. This SLAM approach is enabled by reconstructing the mmWave image of the environment as seen by the base station. Then, an uplink pilot signal from the user is used to estimate both AoA and ToA of the dominant channel paths. Finally, AoA/ToA information is projected into the mmWave image to fully localize the user. Simulations using full-wave electromagnetic solvers are carried out to emulate an environment both in the near and far field. Then, to validate, an experiment carried in laboratory by creating a simple two-dimensional scenario in the 220-300 GHz range using a synthesized 13-cm linear antenna array formed by using vector network analyzer extenders and a one-dimensional linear motorized stage that replicates the base station. After taking measurements, this method successfully reconstructs the image of the environment and localize the user position with centimeter accuracy. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

No description available.

This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the target state is corrected sequentially to the robot correction in the Recursive Bayesian Estimation. In the second stage, with the target being closer, the target state is corrected simultaneously with the robot and the landmarks. The process allows the robot's state uncertainty to be propagated into the estimated target's state, bridging the gap between tracking only methods where the target is estimated assuming known observer state and SLAM methods where only landmarks are considered. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. Also, target belief is always maintained in comparison to temporary tracking methods such as image-tracking. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map entropies in the absence of other landmarks. / M.S. / This thesis presents a generalized framework with the goal of allowing a robot to localize itself and a static target, while building a map of the environment. This map is used as in the Simultaneous Localization and Mapping (SLAM) framework to enhance robot accuracy and with close features. Target, here, is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be continuously observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the robot and close landmarks are estimated simultaneously and they are both corrected. Using the robot's uncertainty in its estimate, the target state is then estimated sequentially, considering known robot state. That decouples the target estimation from the rest of the process. In the second stage, with the target being closer, target, robot and landmarks are estimated simultaneously. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map uncertainties in the absence of other landmarks.

This master thesis explains what is SLAM and reprezents possibilities of implementation. The main goal is to implement chosen algorithm to create map and process measured data from LIDAR sensor.

No description available.

Au sein de la culture lettrée qui est la nôtre, il semble que la littérature soit d'emblée associée à l'écrit. Aussi, les manifestations contemporaines de la littérature orale sont-elles souvent laissées pour compte par les études littéraires, voire dévaluées par l'institution. Or, à une époque où l'on observe une importante résurgence de l'oralité en littérature, il apparaît d'autant plus pertinent de se pencher sur ses nouvelles formes, qui relèvent tout autant de la tradition que d'une modernité caractérisée par l'interdisciplinarité et l'évolution technologique. Dans la mesure où le développement des cultural studies, à la lumière d'une définition extensive de la culture, nous permet de nous intéresser à des pratiques qui n'appartiennent pas à la"haute culture", nous avons pris pour objet le mouvement de slam, qui participe précisément de cette résurgence contemporaine de l'oralité et qui émane, de surcroît, d'une culture"d'en bas". C'est, plus particulièrement, son actualisation québécoise qui constitue le coeur de ce mémoire, bien qu'il ait semblé nécessaire d'en dresser un portrait plus large, vu la méconnaissance relative du phénomène. C'est en 2006 que le mouvement - qui a pris racine au milieu des années 80 aux États-Unis - s'est installé dans la culture francophone du Québec, notamment sous l'impulsion de sa popularisation en France. Il y acquiert rapidement un visage pluriel, fondé sur une ambiguïté quant à la définition même du terme"slam" : s'il désigne à la base un mouvement poétique et social, de même qu'un cadre particulier structurant la pratique de la poésie performée (le slam de poésie), il en vient rapidement à être assimilé à un style, du fait de sa récupération par la culture de masse. Dans un premier chapitre, nous nous attardons à l'histoire du mouvement, ainsi qu'à son évolution aux États-Unis et en France. Dans un deuxième chapitre, nous en retraçons la brève histoire au Québec, avant de dégager les caractéristiques propres de son ancrage québécois. Dans un troisième chapitre, nous analysons la couverture journalistique du slam au Québec, ainsi que sa relation complexe avec l'institution littéraire, et terminons par l'analyse de quatre oeuvres québécoises qui se réclament du slam sans pourtant en adopter le cadre original, afin de cerner leur positionnement en regard du mouvement, de même que les enjeux de la diffusion de l'oralité qu'elles mettent au jour, à l'époque de sa reproductibilité technique.

Bakgrund: Dagens användning av antibakteriellt silver i vardagsprodukter leder till att silverförorenat avlopp hamnar i reningsverken. Ett näringsrikt slam bildas i reningsverken och används som växtnäring på jordbruksland. Ett problem som kan uppstå är att silverföroreningarna ifrån slammet överförs till mark. Eftersom silver är känt för sina antibakteriella egenskaper så är syftet med denna studien att undersöka silvrets effekt på jord och marklevande organismer. Resultat: Den lägsta koncentrationen på 0,56 mg/kg silvret NM-300K hämmade nitritproduktionen i jorden med 16%. Silvernitrat hämmade den mikrobiella massa i jord med 50% efter 7 dygn. Vid lägre silverkoncentrationer på 0,19 mg/kg gynnades nitritproduktionen i mark istället. Antalet markbakterier sjönk i den silverkontaminerade marken i jämförelse med kontrollgruppen. En PVP täckt silvernanopartikel visade sig vara mer toxisk mot nitrifikations bakterier än en silver nanopartikel otäckt yta. Slutsats: Silver har visat sig vara toxisk mot mikroorganismer i mark, dock kan ingen gemensam toxisk koncentration fastställas för alla markorganismer då olika typer av nanosilver och silverföreningar har olika effekt beroende på vilken storlek, form, yta och vad de omvandlas till i naturen.

Since most people now have a high-performing computing device with an attached camera in their pocket, in the form of a smartphone, robotics and computer vision researchers are thrilled about the possibility this creates. Such devices have previously been used in robotics to create 3D maps of environments and objects by feeding the camera data to a 3D reconstruction algorithm. The big downside with smartphones is that their cameras use a different sensor than what is usually used in robotics, namely a rolling shutter camera.These cameras are cheaper to produce but are not as well suited for general 3D reconstruction algorithms as the global shutter cameras typically used in robotics research. One recent, accurate and performance effective 3D reconstruction method which could be used on a mobile device, if tweaked, is LSD-SLAM. This thesis uses the LSD-SLAM method developed for global shutter cameras and incorporates additional methods developed allow the usage of rolling shutter data.The developed method is evaluated by calculating numbers of failed 3D reconstructions before a successful one is obtained when using rolling shutter data.The result is a method which improves this metric with about 70\% compared to the unedited LSD-SLAM method.

Simultaneous localization and mapping (SLAM) is the problem of mapping your surroundings while simultaneously localizing yourself in the map. It is an important and active area of research for robotics. In this master thesis two approaches are attempted to reduce the drift which appears over time in SLAM algorithms. The first approach tries 3 different motion models for the camera. Two of the models exploit the a priori knowledge that the camera is mounted on a trolley. These two methods are shown to improve the results. The second approach attempts to reduce the drift by reducing noise in the point cloud data used for mapping. This is done by finding planar surfaces in the point clouds. Median filtering is used as an alternative to compare the result for noise reduction. The planes estimation approach is also shown to reduce the drift, while the median estimation makes it worse. / Simultaneous localization and mapping (SLAM) är problemet att kartlägga sin omgivning samtidigt som man lokaliserar sig själv i kartan. Det är ett viktigt och aktivt forskningsområde inom robotik. I det här exjobbet testas två tillvägagångssätt för att minska felet i kameraposition och orientering som uppstår över tiden i SLAM-lösningar. Det första tillvägagångssättet testar 3 olika rörelsemodeller för kameran. Två av modellerna utnyttjar vetskapen om att kameran sitter monterad på en vagn. Dessa två metoder förbättrar resultatet för SLAM-algoritmen. Det andra tillväggagångssättet försöker minska felet genom att reducera bruset i punktmolnsdatan som används i kartläggningen. Det görs genom att hitta plana ytor i punktmolnen. Medianfiltrering används som en alternativ lösning för att jämföra hur bra planestimeringen står sig. Planestimeringen visar sig också minska felet i lösningen, medan medianfiltreringen endast försämrar resultatet.