Globally Stable Observers for Simultaneous Localization and Mapping

This thesis focuses on developing observers that can be applied to simultaneous localization and mapping (SLAM), with an emphasis on developing observers that has theoretical proof of convergence. The motivation for this research has been to develop robust navigation methods, that can apply information from the surroundings through on-board sensors, mainly cameras. Applying nonlinear system theory, all the observers have proof of semi global or global asymptotic stability; while globally exponential stability and exponential stability in the large is also shown for some of the observers. Conversely to the main practice in the literature, the observers have been designed on the bearing measurements being represented as unit vector measurements. Two different techniques are applied in the design of the observers or filters. The first technique was based on having an attitude heading reference system available and rearrange the system as a linear time varying system. Astandard Kalman filter could thus be used, and globally exponential stability achieved. A setup where the Kalman filter was combined with a nonlinear observer was also tested, and the combination was able to improve both the accuracy and robustness of the filtering. The second design method, utilized the kinematics of the unit vector pointing at landmarks, and filtered directly the bearing measurements. Inspired by the literature on nonlinear observers, the cross product was used as an innovation term involving the bearing vectors. This proved to give the observer semi-global stability. Through the filtering, it was proved that gyro bias and distance to the landmark could be estimated separately, with the same semi global asymptotic stability, and exponential stability in the large. The fact that the observer designed around unit vector measurements also opened other applications which are highly relevant to visual navigation. A well known fact when performing navigation only based on camera, is that the depth of the scene is ambiguous. This means that all the structure from motion that is estimated from the camera is relative, in fact, the absolute depth in the scene or scale between the camera and real world is unobservable without external information. By normalizing the velocity measured by the camera, similar kinematics as used for the landmark measurements could be applied, and by assuming that a self calibrating IMU with gravity estimate was available, the observer could fuse normalized velocity with the sensor values from the IMU and estimate the metric velocity of the vehicle. This setup also worked with other observers and filters from the literature, and a thorough comparative study was made in simulations and with experimental data, confirming that the velocity estimation was possible. Finally, an observer was expanded to estimate both distance to the landmark and gyro bias simultaneously. With the same global asymptotic stability and exponentially stability in the large. With verifiable conditions for convergence. This made it possible to apply a camera to both gyro bias estimation and estimating distance to the landmark without dealing with problems of initialization or divergence. The setupwas verified both in simulations and on a UAV flight experiment, where the combination of a GNSS, IMU and camera, could perform estimationboth of gyro bias and depth in the camera. Further it was shown how headingand altitude could be estimated or derived from the measurements, such that with the sensor setup proposed, both an altimeter and magnetometer were redundant sensors. There is somework left to prove that camera navigation based on the presented observers is feasible in closed loop or in an industrial setting. Never the less, the thesis has been able to address two important topics in camera aided navigation, namely having a observer using monocular camera handling gyro-bias, and having a setup for velocity estimation provided only a monocular camera and a tactical self calibrating IMU; all with proof of semi-global stability

En kvantitativ studie om outsourcing og backsourcing av IT-tjenester i norske kommuner

Outsourcing er et dagsaktuelt tema, med stor innflytelse på hvordan den norske offentlige sektoren driftes og digitaliseres. Denne oppgaven tar for seg hvordan faktorer innen motivasjon, utfordringer og strategi påvirker norske kommuner når de outsourcer eller backsourcer IT-tjenester. Hensikten er å sammenligne det tilgjengelige teoretiske rammeverket fra dette fagfeltet, med de faktiske forholdene som kommunene opplever. Gjennom oppgaven vil leseren først bli introdusert til det teoretiske rammeverket som oppgaven bygger på, videre vil den metodiske tilnærmingen forklares, og til slutt vil funnene bli presentert og analysert. Oppgaven gjennomføres som en tverrsnittsstudie, med en kvantitativ spørreundersøkelse som datainnsamlingsmetode. Funnene skal gi grunnlag for å svare på følgende forskningsspørsmål: Hvor vanlig er det at kommunene outsourcer og backsourcer IT-aktiviteter? Hva motiverer kommunene til å outsource og backsource IT-aktiviteter? Hva er de vanligste utfordringene til kommunene ved outsourcing av IT-aktiviteter? Har kommunene en tydelig sourcingstrategi? Gjennom oppgaven kartlegges de relevante faktorene for å svare på forskningsspørsmålene. De empiriske funnen settes i sammenheng med det teoretiske perspektivet, og drøftes opp mot dette. Oppgaven konkluderer med en antydning til at majoriteten av norske kommuner benytter seg av outsourcing, mens en liten minoritet har gjennomført backsourcinginitiativer. Innenfor motivasjon viser funnene blant annet at tilgang til ressurser og kompetanse, effektivisering og fokus på egen kjernevirksomhet, er blant de vanligste faktorene. Av utfordringer, opplever kommunene høyere kostnader enn estimert, vanskeligheter rundt sikkerhet og etterlevelse av gjeldende lover, kulturforskjeller og uventede transaksjonskostnader. Funnene antyder at de aller fleste kommuner benytter seg av en sourcingstrategi, men i varierende grad. Samtidig er det få som utarbeider exit-strategier, eller sørger for grundige årlige evalueringer av sourcingstrategien

Nonlinear Adaptive Motion Control and Model-Error Analysis for Ships - Simulations and MCLab experiments

Havet er et upålitelig miljø fult av ulineariteter og forstyrrelser. Dette øker viktigheten av å ha en kontroller som kan håndtere den forandrende dynamikken til skipet for å oppnå førsteklasses styring. Et helhetlig utviklings scenario for en ulinær adaptiv kontroller for et modellskip er presentert. Dette inkluderer design, simulering og eksperimentell verifisering av kontrolleren. En analyse av eksperimentell data er også presentert, hvor både konvensjonelle og nye metoder for å finne forbedringer til simulatoren og kontrolleren er brukt. I denne rapporten er flere varianter av den ulinære adaptive kontrolleren, concurrent learning (CL) utviklet for modellskipet Cybership Enterprice 1 (CSE1). To lagrings algoritmer er foreslått, vindu (WIN) og singulær verdi maksimering (SVD), og to feilsignaler for adapsjonen, z2 og epsilon. Forskjellen i CL controllernes ytelse er utforsket og evaluert gjennom simuleringer. Det er også demmonstrert hvordan kontrollerenes evne til å tilpasse seg modellfeilen ! påvirket ytelsene deres. En komparativ analyse mellom et utvalg av CL controllerne, og allerede velkjente kontrollere adaptiv backstepping (ABS) og backstepping (BS) er også gjennomført. Et kontrollsystem, og simulator for CSE1 ble utviklet. Som forberedelse for eksperimentene i bassenget med CSE1 i Marine Cybernetics Labben (MC-lab), ble et sett med observatore evaluert oppmot hverandre. Et guidance system ble også utviklet, men en sen modifikasjon førte til at den gav ut følge signaler som var fire ganger raskere en tiltenkt. Totalt ble 16 forsøk gjennomført, der 4 kontrollere ble sammenlignet, følgene etter 2 forskjellige baner med 2 set av hastigheter på hver. Dette ble presentert som en komparativ analyse mellom kontrollerne, selv om resultatene gjorde det vanskelig å sammenligne kontrollerne. Imens har fagfeltet multivariat analyse (MVA) også blitt utforsket, og flere metoder og teknikker er presentert i rapporten, samt multivariat statistikk. Disse teknikkene er brukt under analysen av de eksperimentelle dataene, fra å sile data, til og både analysere og lage modeller av !. Partiell minste kvadrats metode(PLS) regresjon er brukt for å lage modellene, og disse ble senere implementert i simulatoren for verifisering. Under analysen av de eksperimentelle dataene, ble mer konvensjonelle metoder også brukt, som system identifikasjon (SysID). Tapt data ble reprodusert i en °apen sløyfe simulator med de eksperimentelle dataene, samt at åpen sløyfe simuleringene av CL controllerne gav bedre innsikt i hva som gikk galt under eksperimentene. CL kontrolleren som brukte viste seg også å fungere til SysID, og dette ble også brukt for å lage en model av !. I tillegg ble en modellering av p°adrags feil også inkludert i SysID

Performance Comparison of Backstepping-Based Adaptive Controllers for Marine Surface Vessels

This paper deals with the design and evaluation of three controllers based on backstepping and different adaptive control schemes, which are applied to the motion control of a nonlinear 3 degrees-of-freedom model of a marine surface vessel. The goal is to make a comparative analysis of the controllers in order to find out which one has the best performance. The considered controllers are: Adaptive backstepping, backstepping with composite concurrent learning and backstepping with cascaded concurrent learning. Numerical simulations are performed for target tracking along an elliptic path, with uncertain vessel model parameters. Motion control performance is evaluated by performance metrics such as IAE and a novel metric named IAEW-WT which combines control accuracy, energy use and actuator wear and tear in one single metric

Estimating vector magnitude from its direction and derivative, with application to bearing-only SLAM filter problem

For some problems, such as monocular visual odometry (VO), vector measurements are given with unknown magnitude. In VO, the magnitude can be found by recognizing features with known position, or with an extra sensor such as an altimeter. This article presents a nonlinear observer that uses the derivative of the vector as an additional measurement for estimating the magnitude of a vector. For the VO example, this means that the velocity can be estimated by fusing the normalized velocity vector with acceleration measurements. The observer exploits the fact that the dynamics of the normalized vector is dependent on the magnitude of the vector. The observer employs methods from nonlinear/adaptive estimation; filters the unit vector on the unit sphere, and retrieves the magnitude of the vector. The observer is shown to be uniformly semi-globally asymptotically (USGAS) stable and uniformly exponentially stable (UES) in a defined region. The observer is applied to the bearing-only SLAM filter problem as an example

Performance comparison of backstepping-based adaptive controllers for marine surface vessels

This paper deals with the design and evaluation of three controllers based on backstepping and different adaptive control schemes, which are applied to the motion control of a nonlinear 3 degrees-of-freedom model of a marine surface vessel. The goal is to make a comparative analysis of the controllers in order to find out which one has the best performance. The considered controllers are: Adaptive backstepping, backstepping with composite concurrent learning and backstepping with cascaded concurrent learning. Numerical simulations are performed for target tracking along an elliptic path, with uncertain vessel model parameters. Motion control performance is evaluated by performance metrics such as IAE and a novel metric named IAEW-WT which combines control accuracy, energy use and actuator wear and tear in one single metric

Cascade Attitude Observer for the SLAM filtering problem

This article presents an attitude observer that exploits both bearing and range measurements from landmarks, in addition to reference vectors such as magnetometer and accelerometer. It is a gyro bias observer in cascade with a simplified complementary filter, driven by a gyro measurement, in which the gyro bias is estimated by comparing the bearing dynamics with the gyro measurements. The observer is compared to a full complimentary filter, and it is shown that it is more robust to initial gyro bias estimation error compared to the complimentary filter. The article also reveals how this new observer handles magnetometer failure and can use landmarks as reference vectors

Semiglobally Asymptotically Stable Nonlinear Observer for Camera Aided Navigation

This brief presents a nonlinear observer that performs range estimation as well as gyro-bias estimation by using velocity, angular rate, and bearing angle measurements from landmarks at unknown locations. The observer is proved to have semiglobal asymptotic stability, and its performance is verified in simulations and on experimental data. The observer is demonstrated on an unmanned aerial vehicle (UAV) with a sensor setup consisting of camera, inertial measurement unit (IMU), and velocity measured by a global navigation satellite system (GNSS). This sensor suite is sufficient to replace the magnetometer and the altimeter

Globally Stable Velocity Estimation Using Normalized Velocity Measurement

The problem of estimating velocity from a monocular camera and calibrated inertial measurement unit (IMU) measurements is revisited. For the presented setup, it is assumed that normalized velocity measurements are available from the camera. By applying results from nonlinear observer theory, we present velocity estimators with proven global stability under defined conditions, and without the need to observe features from several camera frames. Several nonlinear methods are compared with each other, also against an extended Kalman filter (EKF), where the robustness of the nonlinear methods compared with the EKF are demonstrated in simulations and experiments