Tato práce se zabývá vytvořením lokalizačního a navigačního systému mobilního robota pro vnitřní prostředí pomocí frameworku ROS. Stručně je zde představen projekt, v rámci kterého diplomová práce vznikla, a jeho cíle. V rešeršní části je v krátkosti popsán ROS framework, simulační prostředí Gazebo a senzory, kterými robot disponuje. Následuje vytvoření modelu robota a simulačního prostředí, v němž jsou vyzkoušeny lokalizační, navigační a další rutiny. V experimentální části je provedeno testování senzorů a popsáno využití jejich výstupů. Následně jsou upraveny a otestovány algoritmy ze simulace na reálném robotovi. V závěru jsou popsány vytvořené vzdělávací minihry. Hlavním výstupem této práce je funkční stavový automat, který umožňuje manuální ovládání, zadávání cílů pro navigaci a v případě potřeby zajistí autonomní nabití robota.

Ette thèse s’inscrit dans le contexte de la réalité augmentée (RA). La problématique majeure consiste à calculer la pose d’une caméra en temps réel. Ce calcul doit être effectué en respectant trois critères principaux : précision, robustesse et rapidité. Dans le cadre de cette thèse, nous introduisons certaines méthodes permettant d’exploiter au mieux les primitives des images. Dans notre cas, les primitives sont des points que nous allons détecter puis décrire dans une image. Pour ce faire, nous nous basons sur la texture de cette image. Nous avons dans un premier temps mis en place une architecture favorisant le calcul rapide de la pose, sans perdre en précision ni en robustesse. Nous avons pour cela exploité une phase hors ligne, où nous reconstruisons la scène en 3D. Nous exploitons les informations que nous obtenons lors de cette phase hors ligne afin de construire un arbre de voisinage. Cet arbre lie les images de la base de données entre elles. Disposer de cet arbre nous permet de calculer la pose de la caméra plus efficacement en choisissant les images de la base de données jugées les plus pertinentes. Nous rendant compte que la phase de description et de comparaison des primitives n’est pas suffisamment rapide, nous en avons optimisé les calculs. Cela nous a mené jusqu’à proposer notre propre descripteur. Pour cela, nous avons dressé un schéma générique basé sur la théorie de l’information qui englobe une bonne part des descripteurs binaires, y compris un descripteur récent nommé BOLD [BTM15]. Notre objectif a été, comme pour BOLD, d’augmenter la stabilité aux changements d’orientation du descripteur produit. Afin de réaliser cela, nous avons construit un nouveau schéma de sélection hors ligne plus adapté à la procédure de mise en correspondance en ligne. Cela permet d’intégrer ces améliorations dans le descripteur que nous construisons. Procéder ainsi permet d’améliorer les performances du descripteur notamment en terme de rapidité en comparaison avec les descripteurs de l’état de l’art. Nous détaillons dans cette thèse les différentes méthodes que nous avons mises en place afin d’optimiser l’estimation de la pose d’une caméra. Nos travaux ont fait l’objet de 2 publications (1 nationale et 1 internationale) et d’un dépôt de brevet. / This thesis belongs within the context of augmented reality. The main issue resides in estimating a camera pose in real-time. This estimation should be done following three main criteria: precision, robustness and computation efficiency.In the frame of this thesis we established methods enabling better use of image primitives. As far as we are concerned, we limit ourselves to keypoint primitives. We first set an architecture enabling faster pose estimation without loss of precision or robustness. This architecture is based on using data collected during an offline phase. This offline phase is used to construct a 3D point cloud of the scene. We use those data in order to build a neighbourhood graph within the images in the database. This neighbourhood graph enables us to select the most relevant images in order to compute the camera pose more efficiently. Since the description and matching processes are not fast enough with SIFT descriptor, we decided to optimise the bottleneck parts of the whole pipeline. It led us to propose our own descriptor. Towards this aim, we built a framework encompassing most recent binary descriptors including a recent state-of-the-art one named BOLD. We pursue a similar goal to BOLD, namely to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure introduced in BOLD.In this thesis we introduce several methods used to estimate camera poses more efficiently. Our work has been distinguished by two publications (a national and an international one) as well as with a patent application.

Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

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Mobile laser scanning systems automate the acquisition of 3D point clouds of environments. The mapping systems are commonly mounted on cars or ships. This paper presents a flexible mapping solution mounted on an underground vehicle that is able to map underground mines in 3D in walking speeds. A clever choice of hard- and software enables the system to generate 3D maps without using GPS (global positioning system) information and without relying on highly expensive IMU (inertial measurement unit) systems.

Robotic industries are growing faster than in any other era with the demand and rise of in home robots or assisted robots. Such a robot should be able to navigate between different rooms in the house autonomously. For autonomous navigation, the robot needs to build a map of the surrounding unknown environment and localize itself within the map. For home robots, distinguishing between different rooms improves the functionality of the robot. In this research, Simultaneously Localization And Mapping (SLAM) utilizing a LiDAR sensor is used to construct the environment map. LiDAR is more accurate and not sensitive to light intensity compared to vision. The SLAM method used is Gmapping to create a map of the environment. Gmapping is one of the robust and user-friendly packages in the Robotic Operating System (ROS), which creates a more accurate map, and requires less computational power. The constructed map is then used for room categorization using Convolutional Neural Network (CNN). Since CNN is one of the powerful techniques to classify the rooms based on the generated 2D map images. To demonstrate the applicability of the approach, simulations and experiments are designed and performed on campus and an apartment environment. The results indicate the Gmapping provides an accurate map. Each room used in the experimental design, undergoes training by using the Convolutional Neural Network with a data set of different apartment maps, to classify the room that was mapped using Gmapping. The room categorization results are compared with other approaches in the literature using the same data set to indicate the performance. The classification results show the applicability of using CNN for room categorization for applications such as assisted robots.

Ground Penetrating Radar (GPR) is a tool for mapping the subsurface in a non-invasive way. GPR surveys are currently carried out manually; a time-consuming, tedious and sometimes dangerous task. This report presents the high-level software components for an autonomous unmanned ground vehicle to conduct GPR surveys. The hardware system is a four-wheel drive, skid steering, battery operated vehicle with integrated GPR equipment. Autonomous surveys are conducted using lidar-inertial odometry with robust path planning, path following and obstacle avoidance capabilities. Evaluation shows that the vehicle is able to autonomously execute a planned survey with high accuracy and stops before collisions occur. This system enables high-frequency surveys to monitor the evolution of an area over time, allows one operator to monitor multiple surveys at once, and facilitates future research into novel survey patterns that are difficult to follow manually

This Master thesis tackles the problem of measuring objects’ directions from a motionless observation point. A new method based on a single rotating camera requiring the knowledge of only two (or more) landmarks’ direction is proposed. In a first phase, multi-view geometry is used to estimate camera rotations and key elements’ direction from a set of overlapping images. Then in a second phase, the direction of any object can be estimated by resectioning the camera associated to a picture showing this object. A detailed description of the algorithmic chain is given, along with test results on both synthetic data and real images taken with an infrared camera. / Detta masterarbete behandlar problemet med att mäta objekts riktningar från en fast observationspunkt. En ny metod föreslås, baserad på en enda roterande kamera som kräver endast två (eller flera) landmärkens riktningar. I en första fas används multiperspektivgeometri, för att uppskatta kamerarotationer och nyckelelements riktningar utifrån en uppsättning överlappande bilder. I en andra fas kan sedan riktningen hos vilket objekt som helst uppskattas genom att kameran, associerad till en bild visande detta objekt, omsektioneras. En detaljerad beskrivning av den algoritmiska kedjan ges, tillsammans med testresultat av både syntetisk data och verkliga bilder tagen med en infraröd kamera.

<div><div><div><p>Globally, there are more than 727 million people aged 65 years and older in the world, and the elderly population is expected to grow more than double in the next three decades. Families search for affordable and quality care for their senior loved ones will have an effect on the care-giving profession. A personal robot assistant could help with daily tasks such as carrying things for them and keeping track of their routines, relieving the burdens of human caregivers. Performing mentioned tasks usually requires the robot to autonomously navi- gate. An autonomous navigation robot should collect the knowledge of its surroundings by mapping the environment, find its position in the map and calculate trajectories by avoiding obstacles. Furthermore, to assign specific tasks which are in various locations, robot has to categorize the rooms in addition to memorizing the respective coordinates. In this research, methods have been developed to achieve autonomous navigation and room categorization of a mobile robot within indoor environments. A Simultaneously Localization and Map- ping (SLAM) algorithm has been used to build the map and localize the robot. Gmapping, a method of SLAM, was applied by utilizing an odometry and a 2D Light Detection and Ranging (LiDAR) sensor. The trajectory to achieve the goal position by an optimal path is provided by path planning algorithms, which is divided into two parts namely, global and local planners. Global path planning has been produced by DIJKSTRA and local path planning by Dynamic Window Approach (DWA). While exploring new environments with Gmapping and trajectory planning algorithms, rooms in the generated map were classified by a powerful deep learning algorithm called Convolutional Neural Network (CNN). Once the environment is explored, the robots localization in the 2D space is done by Adaptive Monte Carlo Localization (AMCL). To utilize and test the methods above, Gazebo software by The Robotic Operating System (ROS) was used and simulations were performed prior to real life experiments. After the trouble-shooting and feedback acquired from simulations, the robot was able to perform above tasks and later tested in various indoor environments. The environment was mapped successfully by Gmapping and the robot was located within the map by AMCL. Compared to the theoretical maximum efficient path, the robot was able to plan the trajectory with acceptable deviation. In addition, the room names were classified with minimum of 85% accuracy by CNN algorithm. Autonomous navigation results show that the robot can assist elderly people in their home environment by successfully exploring, categorizing and navigating between the rooms.</p></div></div></div>

Mobile robotics has been very discussed and wide spread topic recently.   This due to the development in the computer technology that allows us to create   better and more sophisticated robots. The goal of this effort is to create robots   that will be able to autonomously move in the chosen environment. To achieve this goal,   it is necessary for the robot to create the map of its environment, where   the motion planning will occur. Nowadays, the probabilistic algorithms based   on the SLAM algorithm are considered standard in the mapping in these times.   This Phd. thesis deals with the proposal of the motion planning of the robot with   stereocamera placed on the pan-and-tilt unit. The motion planning is designed with   regard to the use of algorithms, which will look for the significant features   in the pair of the images. With the use of the triangulation the map, or a model will be created.     The benefits of this work can be divided into three parts. In the first one the way   of marking the free area, where the robot will plan its motion, is described. The second part   describes the motion planning of the robot in this free area. It takes into account   the properties of the SLAM algorithm and it tries to plan the exploration in order to create   the most precise map. The motion of the pan-and-tilt unit is described in the third part.   It takes advantage of the fact that the robot can observe places that are in the different   directions than the robot moves. This allows us to observe much bigger space without   losing the information about the precision of the movements.