The goal of this work has been to improve the accuracy of a pre-existing algorithm for vehicle pose estimation, which uses intrinsic measurements of vehicle motion and measurements derived from far infrared images. Estimating the pose of a vehicle, based on images from an on-board camera and intrinsic measurements of vehicle motion, is a problem of simultanoeus localization and mapping (SLAM), and it can be solved using the extended Kalman filter (EKF). The EKF is a causal filter, so if the pose estimation problem is to be solved off-line acausal methods are expected to increase estimation accuracy significantly. In this work the EKF has been compared with an acausal method for solving the SLAM problem called smoothing and mapping (SAM) which is an optimization based method that minimizes process and measurement noise. Analyses of how improvements in the vehicle motion model, using a number of different model extensions, affects accuracy of pose estimates have also been performed.

Detta examensarbete behandlar slamhanteringen på Sibbhults avloppsreningsverk (Sarv). I denna rapport tas det upp hur avvattningen och förtjockningen av slammet går till i dagsläget och vilka förändringar som kan göras för att förbättra dessa processer. Här visas även vilka eventuella vinster som kan fås med förändringar och eventuella investeringar i en avvattningsapplikation. Redan med små medel kan förtjockningen av slammet förändras, även om det inte införskaffas en mekanisk avvattnare, som t ex med inblandning av polymerer i avloppsvattnet. För att finna olika fungerande alternativ har information insamlats från flera företag som tillhandahåller applikationer för slamavvattnare. Information har även införskaffats genom egen erfarenhet under min verksamhetsförlagda utbildnings (VFU) period på SArv, och vid diskussion med personal som arbetar vid vatten- och avloppsreningsverken i Östra Göinge kommun.

Localization and mapping are two of the most central tasks when it comes toautonomous robots. It has often been performed using expensive, accurate sensorsbut the fast development of consumer electronics has made similar sensorsavailable at a more affordable price. In this master thesis a TurtleBot\texttrademark\, robot and a MicrosoftKinect\texttrademark\, camera are used to perform Simultaneous Localization AndMapping, SLAM. The thesis presents modifications to an already existing opensource SLAM algorithm. The original algorithm, based on visual odometry, isextended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter,EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF areimplemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set isrecorded in an ordinary office room which constitutes an environment with manylandmarks. The other set is recorded in a conference room where one of the wallsis flat and white. This gives a partially sparse featured environment. The result by providing additional sensor information is a more robust algorithm.Periods without credible visual information does not make the algorithm lose itstrack and the algorithm can thus be used in a larger variety of environmentsincluding such where the possibility to extract landmarks is low. The resultalso shows that the visual odometry can cancel out drift introduced bywheel odometry and gyro sensors.

Successful and high precision localization is an important feature for autonomous vehicles in an urban environment. GPS solutions are not good on their own and laser, sonar and radar are often used as complementary sensors. Localization with these sensors requires the use of techniques grouped under the acronym SLAM (Simultaneous Localization And Mapping). These techniques work by comparing the current sensor inputs to either an incrementally built or known map, also adding the information to the map.Most of the SLAM techniques assume the environment to be static, which means that dynamics and clutter in the environment might cause SLAM to fail. To ob-tain a more robust algorithm, the dynamics need to be dealt with. This study seeks a solution where measurements from different points in time can be used in pairwise comparisons to detect non-static content in the mapped area. Parked cars could for example be detected at a parking lot by using measurements from several different days.The method successfully detects most non-static objects in the different test datasets from the sensor. The algorithm can be used in conjunction with Pose-SLAM to get a better localization estimate and a map for later use. This map is good for localization with SLAM or other techniques since only static objects are left in it.

Som följd av utökat tillstånd för produktion vid Billerud AB:s pappersbruk, Gruvön, i Grums har bruket byggt en ny avloppsreningsanläggning med flerstegsrening. Den tidigare reningsanläggningen, en luftad damm belägen i Ålviken i anslutning till Vänern, kommer därmed att stängas. Syftet med föreliggande rapport har därför varit att undersöka huruvida dammen behöver hållas aktiv genom luftning en tid framöver för att gynna nedbrytning av organiskt material samt undvika uppkomsten av illaluktande gaser. Nedbrytningshastigheten har studerats med hjälp av glödförlust, COD-tester och alkalinitet. COD-testerna genomfördes enligt ampullmetoden Hach-Lange i miljölabbet vid Gruvöns bruk. Alkaliniteten har bestämts genom titrering av svavelsyra. Försöken gjordes under en tidsperiod på tre månader. Slam hämtades från tre provpunkter i dammen och delades upp i en anläggning för aerobisk nedbrytning samt för anaerobisk nedbrytning. De resultat som framkom vid undersökningarna var att nedbrytningen i samtliga provpunkter sker långsamt. Genom linjärregression har tiden för nedbrytningen beräknats. För att bryta ner det organiska materialet i dammen skulle det, enligt den här metoden, ta ca 3 år för den anaeroba processen samt 15 år för den aeroba processen. Den stora tidsskillnaden här emellan beror till stor del på var i dammen slammet är upphämtat. Att stänga av luftarna i dammen efter att avloppsvattenflödet stängts av innebär energimässiga och ekonomiska vinster. Den anaeroba nedbrytningen som följer därefter kommer då att skapa illaluktande gaser såsom svavelväte och metangas. / As a result of increased permission to produce paper and paper pulp at the paper mill Gruvön AB in Grums the mill has built a multiple step waste water treatment construction. The previous treat construction was an aerated lagoon located in Vänern which now will be shut down. The aim of this report is to examine whether this aerated lagoon needs to be aereated to keep the decomposition of the organic material and/or to avoid origin of odours. The decomposition rate is studied by loss on ignition, COD-tests and alcalinity. The COD-tests was performed in the environmental lab at Gruvön by the ampoule method according to Hach-Lange. The alcalinity was determined by titration with sulfuric acid. The experiments were performed during a period of three months. Sludge was collected from three test points from the lagoon. The sludge was then separated into one construction to measure the aerobic decomposition and one to measure the anaerobic decomposition. The results show that the decomposition rate is low. The decomposition rate has been evaluated by linear regression. This method indicates that it would take about three years for the anaerobic process and about 15 years for the aerobic process to degrade all the organic compound. The difference in time between the two processes is probably dependent on for example the test points localization in the lagoon. To turn off the aerators of the lagoon could save energy and gain economical profits. The anaerobic decomposition that begins after closing down the aerators could result in creation of chemical odours such as hydrogen sulphide and methane.

In this dissertation, the mapping of outdoor environments and localization of a mobile robot in that setting is considered. It is well known that in the absence of a map or precise pose estimates, localization and mapping is a coupled problem. However, in this dissertation this problem is decoupled in to two disjoint steps / mapping and localization on the acquired map. First the images of the outdoor environment is downloaded from a website such as Google Earth and then these images are processed by utilizing several artificial neural network topologies to create maps. Once these maps are obtained, the localization is done by using Monte Carlo localization. This dissertation addresses a solution for the information which is most of the time taken for granted in most studies / a prior map of environment. Mapping is solved by using a novel approach / the map of the environment is created by processing satellite images. Several global localization techniques are developed and evaluated to be used with these map so as to localize a mobile robot globally. The outcome of this novel approach presented here may serve as a virtual GPS. Mobile phone applications can localize a user within a circle of uncertainty without GPS. This crude localization may be used to download relevant satellite images of the local environment. Once the mobile robot is localized on the map created from the satellite images by using available techniques in the literature i.e. Monte Carlo localization, it may be claimed that it is localized on Earth.

A wearable robot should know its environment and its location in order to help its operator. Wearable robots are becoming more feasible with the development of more powerful and smaller computing devices and cameras. The main aim of this research is to build a wearable robot with a low cost stereo camera system which explores a room sized unknown environment online and automatically. To achieve 3D localization and map building for the wearable robot, a consistent visual-SLAM algorithm is implemented by using point features in the environment and Extended Kalman Filter for state estimation. The whole system includes camera models and calibration, feature extraction, depth measurement and Extended Kalman Filter algorithm. Moreover, a map management algorithm is developed. This algorithm keeps the number of features spatially uniform in the scene and adds new features when feature number decreases in a frame. Furthermore, a user-interface is presented so that the location of the camera,the features and the constructed map are visualized online. Most importantly, the system is conducted by a low-cost stereo system.

Large-scale mapping has become the key to numerous applications, e.g. simultaneous localization and mapping (SLAM) for autonomous robots. Despite of the success of many SLAM projects, there are still some challenging scenarios in which most of the current algorithms are not able to deliver an exact solution fast enough. One of these challenges is the size of SLAM problems, which has increased by several magnitudes over the last decade. Another challenge for SLAM problems is the large amount of noise baked in the measurements, which often yields poor initializations and slows or even fails the optimization. Urban 3D reconstruction is another popular application for large-scale mapping and has received considerable attention recently from the computer vision community. High-quality 3D models are useful in various successful cartographic and architectural applications, such as Google Earth or Microsoft Live Local. At the heart of urban reconstruction problems is structure from motion (SfM). Due to the wide availability of cameras, especially on handhold devices, SfM is becoming a more and more crucial technique to handle a large amount of images. In the thesis, I present a novel batch algorithm, namely Tectonic Smoothing and Mapping (TSAM). I will show that the original SLAM graph can be recursively partitioned into multiple-level submaps using the nested dissection algorithm, which leads to the cluster tree, a powerful graph representation. By employing the nested dissection algorithm, the algorithm greatly minimizes the dependencies between two subtrees, and the optimization of the original graph can be done using a bottom-up inference along the corresponding cluster tree. To speed up the computation, a base node is introduced for each submap and is used to represent the rigid transformation of the submap in the global coordinate frame. As a result, the optimization moves the base nodes rather than the actual submap variables. I will also show that TSAM can be successfully applied to the SfM problem as well, in which a hypergraph representation is employed to capture the pairwise constraints between cameras. The hierarchical partitioning based on the hypergraph not only yields a cluster tree as in the SLAM problem but also forces resulting submaps to be nonsingular. I will demonstrate the TSAM algorithm using various simulation and real-world data sets.

<p>Navigation with unmanned aerial vehicles (UAVs) requires good knowledge of the current position and other states. A UAV navigation system often uses GPS and inertial sensors in a state estimation solution. If the GPS signal is lost or corrupted state estimation must still be possible and this is where simultaneous localization and mapping (SLAM) provides a solution. SLAM considers the problem of incrementally building a consistent map of a previously unknown environment and simultaneously localize itself within this map, thus a solution does not require position from the GPS receiver.</p><p>This thesis presents a visual feature based SLAM solution using a low resolution video camera, a low-cost inertial measurement unit (IMU) and a barometric pressure sensor. State estimation in made with a extended information filter (EIF) where sparseness in the information matrix is enforced with an approximation.</p><p>An implementation is evaluated on real flight data and compared to a EKF-SLAM solution. Results show that both solutions provide similar estimates but the EIF is over-confident. The sparse structure is exploited, possibly not fully, making the solution nearly linear in time and storage requirements are linear in the number of features which enables evaluation for a longer period of time.</p>

Dans cette thèse, nous résolvons le problème de reconstruire simultanément une représentation de la géométrie du monde, de la trajectoire de l'observateur, et de la trajectoire des objets mobiles, à l'aide de la vision. Nous divisons le problème en trois étapes : D'abord, nous donnons une solution au problème de la cartographie et localisation simultanées pour la vision monoculaire qui fonctionne dans les situations les moins bien conditionnées géométriquement. Ensuite, nous incorporons l'observabilité 3D instantanée en dupliquant le matériel de vision avec traitement monoculaire. Ceci élimine les inconvénients inhérents aux systèmes stéréo classiques. Nous ajoutons enfin la détection et suivi des objets mobiles proches en nous servant de cette observabilité 3D. Nous choisissons une représentation éparse et ponctuelle du monde et ses objets. La charge calculatoire des algorithmes de perception est allégée en focalisant activement l'attention aux régions de l'image avec plus d'intérêt.