Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below. We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship. Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined. The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications. We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM. We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity. / LINK-SIC

Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.

This paper describe design and implementation of SLAM algorithm for selflocalization and mapping in indoor environment using data from laser scanner. Design is focused on 2D variant of SLAM, but parts is purposely reliazed to be usable in 3D SLAM. This ability is demonstrated at the end of paper.

In pursuit of creating a fully automated navigation system that is capable of operating in dynamic environments, a large amount of research is being devoted to systems that use visual odometry assisted methods to estimate the position of a platform with regards to the environment surrounding it. This includes systems that do and do not know the environment a priori, as both rely on the same methods for localisation. For the combined problem of localisation and mapping, Simultaneous Localisation and Mapping (SLAM) is the de facto choice, and in recent years with the advent of color and depth (RGB-D) sensors, RGB-D SLAM has become a hot topic for research. Most research being performed is on improving the overall system accuracy or more specifically the performance with regards to the overall trajectory error. While this approach quantifies the performance of the system as a whole, the individual frame-to-frame performance is often not mentioned or explored properly. While this will directly tie in to the overall performance, the level of scene cohesion experienced between two successive observations can vary greatly over a single dataset of observations. The focus of this dissertation will be the relevant levels of translational and rotational velocities experienced by the sensor between two successive observations and the effect on the final accuracy of the SLAM implementation. The frame rate will specifically be used to alter and evaluate the different spatial velocities experienced over multiple datasets of RGB-D data. Two systems were developed to illustrate and evaluate the potential of various approaches to RGB-D SLAM. The first system is a real-world implementation where SLAM is used to localise and map the environment surrounding a quadcopter platform. A Microsoft Kinect is directly mounted to the quadcopter and is used to provide a RGB-D datastream to a remote processing terminal. This terminal runs a SLAM implementation that can alternate between different visual odometry methods. The remote terminal acts as the position controller for the quadcopter, replacing the need for a direct human operator. A semi-automated system is implemented, that allows a human operator to designate waypoints within the environment that the quadcopter moves to. The second system uses a series of publicly available RGB-D datasets with their accompanying ground-truth readings to simulate a real RGB-D datasteam. This is used to evaluate the performance of the various RGB-D SLAM approaches to visual odometry. For each of the datasets, the accompanying translational and angular velocity on a frame-to-frame basis can be calculated. This can, in turn, be used to evaluate the frame-to-frame accuracy of the SLAM implementation, where the spatial velocity can be manually altered by occluding frames within the sequence. Thus, an accurate relationship can be calculated between the frame rate, the spatial velocity and the performance of the SLAM implementation. Three image processing techniques were used to implement the visual odometry for RGB-D SLAM. SIFT, SURF and ORB were compared across eight of the TUM database datasets. SIFT had the best performance, with a 30% increase over SURF and doubling the performance of ORB. By implementing SIFT using CUDA, the feature detection and description process only takes 18ms, negating the disadvantage that SIFT has compared to SURF and ORB. The RGB-D SLAM implementation was compared to four prominent research papers, and showed comparable results. The effect of rotation and translation was evaluated, based on the effect of each rotation and translation axis. It was found that the z-axis (scale) and the roll-axis (scene orientation) have a lower effect on the average RPE error in a frame-to-frame basis. It was found that rotation has a much greater impact on the performance, when evaluating rotation and translation separately. On average, a rotation of 1deg resulted in a 4mm translation error and a 20% rotation error , where a translation of 10mm resulted in a rotation error of 0.2deg and a translation error of 45%. The combined effect of rotation and translation had a multiplicative effect on the error metric. The quadcopter platform designed to work with the SLAM implementation did not function ideally, but it was sufficient for the purpose. The quadcopter is able to self stabilise within the environment, given a spacious area. For smaller, enclosed areas the backdraft generated by the quadcopter motors lead to some instability in the system. A frame-to-frame error of 40.34mm and 1.93deg was estimated for the quadcopter system. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

In der heutigen Zeit spielt die Automatisierung eine immer bedeutendere Rolle, speziell im Bereich der Robotik entwickeln sich immer neue Einsatzgebiete, in denen der Mensch durch autonome Fahrzeuge ersetzt wird. Dabei orientiert sich der Großteil der eingesetzten Roboter an Streckenmarkierungen, die in den Einsatzumgebungen installiert sind. Bei diesen Systemen gibt es jedoch einen hohen Installationsaufwand, was die Entwicklung von Robotersystemen, die sich mithilfe ihrer verbauten Sensorik orientieren, vorantreibt. Es existiert zwar eine Vielzahl an Robotern die dafür verwendet werden können. Die Entwicklung der Steuerungssoftware ist aber immer noch Teil der Forschung. Für die Steuerung wird eine Umgebungskarte benötigt, an der sich der Roboter orientieren kann. Hierfür eignen sich besonders SLAM-Verfahren, die simultanes Lokalisieren und Kartographieren durchführen. Dabei baut der Roboter während seiner Bewegung durch den Raum mithilfe seiner Sensordaten eine Umgebungskarte auf und lokalisiert sich daran, um seine Position auf der Karte exakt zu bestimmen. Im Laufe dieser Arbeit wurden über 30 verschiedene SLAM Implementierungen bzw. Umsetzungen gefunden die das SLAM Problem lösen. Diese sind jedoch größtenteils an spezielle Systembzw. Umgebungsvoraussetzungen angepasste eigenständige Implementierungen. Es existiert keine öffentlich zugängliche Übersicht, die einen Vergleich aller bzw. des Großteils der Verfahren, z.B. in Bezug auf ihre Funktionsweise, Systemvoraussetzungen (Sensorik, Roboterplattform), Umgebungsvoraussetzungen (Indoor, Outdoor, ...), Genauigkeit oder Geschwindigkeit, gibt. Viele dieser SLAMs besitzen Implementierungen und Dokumentationen in denen ihre Einsatzgebiete, Testvoraussetzungen oder Weiterentwicklungen im Vergleich zu anderen SLAMVerfahren beschrieben werden, was aber bei der großen Anzahl an Veröffentlichungen das Finden eines passenden SLAM-Verfahrens nicht erleichtert. Bei einer solchen Menge an SLAM-Verfahren und Implementierungen stellen sich aus softwaretechnologischer Sicht folgende Fragen: 1. Besteht die Möglichkeit einzelne Teile des SLAM wiederzuverwenden? 2. Besteht die Möglichkeit einzelne Teile des SLAM dynamisch auszutauschen? Mit dieser Arbeit wird das Ziel verfolgt, diese beiden Fragen zu beantworten. Hierfür wird zu Beginn eine Übersicht über alle gefundenen SLAMs aufgebaut um diese in ihren grundlegenden Eigenschaften zu unterscheiden. Aus der Vielzahl von Verfahren werden die rasterbasierten Verfahren, welche Laserscanner bzw. Tiefenbildkamera als Sensorik verwenden, als zu untersuchende Menge ausgewählt. Diese Teilmenge an SLAM-Verfahren wird hinsichtlich ihrer nichtfunktionalen Eigenschaften genauer untersucht und versucht in Komponenten zu unterteilen, welche in mehreren verschiedenen Implementierungen wiederverwendet werden können. Anhand der extrahierten Komponenten soll ein Featurebaum aufgebaut werden, der dem Anwender einen Überblick und die Möglichkeit bereitstellt SLAM-Verfahren nach speziellen Kriterien (Systemvoraussetzungen, Umgebungen, ...) zusammenzusetzen bzw. zur Laufzeit anzupassen. Dafür müssen die verfügbaren SLAM Implementierungen und dazugehörigen Dokumentationen in Bezug auf ihre Gemeinsamkeiten und Unterschiede analysiert werden.

Kommunal avloppsvattenrening är en energikrävande verksamhet. Det beror till stor del på att avloppsvattnet behöver transporteras med hjälp av pumpar och att den biologiska reningen kräver syretillförsel. Reningen ger även upphov till ett slam och behandlingen av detta kräver ytterligare energi. Samtidigt kan det energirika slammet utnyttjas för att alstra energi. Att minska energiförbrukningen är till nytta både för samhället och för miljön. Syftet med detta examensarbete var att utveckla ett enkelt verktyg som stöd till personer som arbetar med avloppsvattenrening. De ska få en helhetsbild över verkets energiförbrukning. Därför har ett konfigurerbart visualiseringsverktyg över mass- och energiflöden för ett avloppsreningsverk tagits fram i Excel. Även om insamlad data kommer från befintliga reningsverk och litteraturstudier, kan användaren själv justera flera parametrar för anpassning till egna erfarenheter. Arbetet är utfört på initiativ av och i nära samarbete med IVL – Svenska Miljöinstitutet. Det är delvis baserat på ett tidigare examensarbete, som samlat in och kartlagt energiflöden utifrån ett antal svenska och utländska studier. Verktyget visar mass- och energiflöden genom biologisk rening och slambehandling i form av rötning och avvattning. Även utökad slambehandling i form av torkning, förbränning och förgasning undersöks ur ett energiperspektiv. Dessutom studeras hur teknik för bättre energiutnyttjande – värmeväxlare, värmepumpar och kraftvärmeverk – kan användas för att tillvarata energi i vatten, slam och producerad bio- och syntesgas. Resultatet visar att värmepumpar för utgående renat vatten är en energieffektiv metod, som fler verk kan använda sig av. Med tekniken täcks det interna värmebehovet och överskottet kan säljas till fjärrvärme. Att använda producerad gas i kraftvärmeverk för intern el- och värmeproduktion är också ett alternativ. Det får dock vägas mot möjligheten att uppgradera gasen till fordonsgaskvalitet. Både förbränning och förgasning är intressanta alternativ för vidare slambehandling, även om förgasning fortfarande är på utvecklingsstadiet. / Municipal wastewater treatment is an energy intensive activity. It depends largely on the need for pumps for transporting the sewage water and the aeration of the biological treatment. The process produces sludge, and treating it demands even more energy. But the energy-rich sludge may also be utilized for energy production. A lower energy-cost is of value for society and the environment. The purpose of this thesis was to develop a simple tool as support for employees at wastewater treatment plants (WWTP). They shall get a holistic view of their power consumption. A configurable visualization tool showing mass- and energy flows has therefore been constructed in Excel. The tool is based on collected data from actual WWTPs and literature studies, but the user can adjust several parameters for adaptation to personal experiences. The thesis is carried out at the initiative of and in close collaboration with IVL – Swedish Environmental Research Institute. It is a continuation of an earlier master thesis, which gathered data and mapped energy flows based on a number of Swedish and foreign studies. The tool shows mass and energy flows through biological treatment and sludge treatment with anaerobic digestion and dewatering. Enhanced sludge treatment by drying, incineration and gasification is also investigated from an energy perspective. Technology for energy efficiency – heat exchangers, heat pumps and combined heat and power plants – are studied for their ability of collecting energy from water, sludge and bio- or syngas. Result shows that using heat pumps for clean water leaving the treatment plant are an energy effective solution, which may be further implemented. The solution covers internal heat demand and surplus heat may be sold as district heating. Another alternative is to send produced gas to a combined heat and power plant for electricity and heat production, although it may be more favorable to upgrade the gas to vehicle gas quality. Both incineration and gasification are interesting alternatives for further sludge treatment, although gasification is an “unproven” technology.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Monocular simultaneous localization and mapping (SLAM) is an important technique that enables very inexpensive environment mapping and pose estimation in small systems such as smart phones and unmanned aerial vehicles. However, the information generated by monocular SLAM is in an arbitrary and unobservable scale, leading to drift and making it difficult to use with other sources of odometry for control or navigation. To correct this, the odometry needs to be aligned with metric scale odometry from another device, or else scale must be recovered from known features in the environment. Typically known environmental features are not available, and for systems such as cellphones or unmanned aerial vehicles (UAV), which may experience sustained, small scale, irregular motion, an IMU is often the only practical option. Because accelerometers measure acceleration and gravity, an inertial measurement unit (IMU) must filter out gravity and track orientation with complex algorithms in order to provide a linear acceleration measurement that can be used to recover SLAM scale. In this thesis, an alternative method will be proposed, which detects and removes gravity from the accelerometer measurement by using the unscaled direction of acceleration derived from the SLAM odometry.

The aim of this thesis is to investigate how well a generic monocular camera, placed on the vehicle, can be employed to localize an autonomous vehicle in a warehouse setting. The main function is to ascertain which zone the vehicle is currently in, as well as update the status when entering a new zone. Two zones are defined, where one has a lower allowed top velocity and the other a higher one. For this purpose ArUco markers are used to signal the system as to where it currently is. Markers are strategically placed around the laboratory area to saturate the environment with possible detections. Multiple sequences are recorded while varying camera placement, angles, and paths to determine the optimal number and placement of markers. In addition to this, a SLAM solution is tested in order to explore what benefits can be found. The idea is to provide fine-grained localization as well as a map of the warehouse environment, to provide more options for further development. To solve the SLAM problem, an implemented particle filter approach initializes a set of particles uniformly distributed within the world frame. For each frame, the particles undergo pose prediction, weight assignment based on likelihood, and resampling. This iterative process gradually converges the particles toward the camera's true position. Visual odometry techniques are used to estimate the camera's ego-motion. The process involves acquiring a sequence of images, detecting distinctive features, matching features between consecutive frames, estimating camera motion, and optionally applying local optimization techniques for further refinement. The implementation shows promise and all test cases performed during the project have been successful as for the zone localization. The SLAM solution can detect and track specific features or landmarks over consecutive frames. By triangulating the positions of these features, their depth and distance can be determined. However, the visualization of these features on a top-down map, which was part of the plan, has not been completed yet despite finishing the particle filter implementation.

Micro Aerial Vehicles (MAVs) provide a highly capable, agile platform, ideally suited for intelligence/surveillance/reconnaissance missions, urban search and rescue, and scientific exploration. Critical to the success of these tasks is a system which moves au-tonomously through an unknown, obstacle-strewn, GPS-denied environment. Classical simultaneous localization and mapping (SLAM) approaches rely on large, heavy sensors to generate 3-D information about a MAV’s surroundings, severely limiting its abilities. This motivates a study of Parallel Tracking and Mapping (PTAM), an algorithm requiring only a single camera to provide 3-D data to an autonomous navigation system. Metric properties of 3-D MAV pose estimates are compared with physical measurements to ex-plore tracking accuracy. Additionally, a discrete wavelet transform-based keypoint detec-tor is implemented for a feasibility study on improving map density in low-visual-detail environments. Finally, a system is presented that integrates PTAM, autonomous MAV control, and a human interface for manual control and data logging.