The increased market for navigation, localization and mapping system has encouraged the research to dig deeper into these new and challenging areas. The remarkable development of computer soft- and hardware have also opened up many new doors. Things which more or less where impossible ten years ago are now reality. The possibilities of using a mathematical approach to compensate for the need of expensive sensors has been one of the main objectives in this thesis. Here you will find the basic principles of localization of indoor UAVs using particle filter (PF) and Octomaps, but also the procedures of implementing 2D scanmatching algorithms and quaternions. The performance of the algorithms is evaluated using a high precision motion capture system. The UAV which forms the basis for this thesis is equipped with a 2D laser and an inertial measurement unit (IMU). The results show that it is possible to perform localization in 2D with centimetre precision only by using information from a laser and a predefined Octomap.

Autonomous navigation of our rough-terrain rovers implies the need of a good representation of their near surrounding. In order to archive this we fuse several of their sensors into one representation called OctoMap. But moving obstacles can produce artefacts, leading to untraversable re- gions. Furthermore the map itself is increasing in size while discovering new places. Even though we are only interested in the near surrounding of the rovers. Our approach to these problems is the usage of timestamps within the map. If a certain region was not updated within a given interval, it will be set to free space or deleted from the map. This first option is an existing solution and the second option reflects our new alternative.

Autonomous navigation of our rough-terrain rovers implies the need of a good representation of their near surrounding. In order to archive this we fuse several of their sensors into one representation called OctoMap. But moving obstacles can produce artefacts, leading to untraversable re- gions. Furthermore the map itself is increasing in size while discovering new places. Even though we are only interested in the near surrounding of the rovers. Our approach to these problems is the usage of timestamps within the map. If a certain region was not updated within a given interval, it will be set to free space or deleted from the map. This first option is an existing solution and the second option reflects our new alternative.

<p>Today, computer science and robotics have been highly developed. Simultaneous Localization and Mapping (SLAM) is widely used in mobile robot navigation, game design, and autonomous vehicles. It can be said that in the future, most scenarios where mobile robots are applied will require localization and mapping. Among them, the construction of three-dimensional(3D) maps is particularly important for environment visualization which is the focus of this research.</p> <p>In this project, the data used for visualization was collected using a vision sensor. The data collected by the vision sensor is processed by ORB-SLAM2 to generate the 3D cloud point maps of the environment. Because, there are a lot of noise in the map points cloud, filters are used to remove the noise. The generated map points are processed by the straight-through filter to cut off the points out of the specific range. Statistical filters are then used to remove sparse outlier noise. Thereafter, in order to improve the calculation efficiency and retain the necessary terrain details, a voxel filter is used for downsampling. In order to improve the composition effect, it is necessary to appropriately increase the sampling amount to increase surface smoothness. Finally, the processed map points are visualized using Octomap. The implementation utilizes the services provided by the Robot Operating System (ROS). The powerful Rviz software on the ROS platform is used. The processed map points as cloud data are published in ROS and visualized using Octomap. </p> <p>Simulation results confirm that Octomap can show the terrain details well in the 3D visualization of the environment. After the simulations, visualization experiments for two environments of different complexity are performed. The experimental results show that the approach can mitigate the influence of noise on the visualization results to a certain extent. It is shown that for static high-precision point clouds, Octomap provides a good visualization. The simulation and experimental results demonstrate the applicably of the approach to visualize 3D map points for the purpose of autonomous navigation.</p><br>

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This thesis describes all the necessary parts needed to build a complete system for autonomous indoor mapping in 3D. The robotic platform used is a two-wheeled Segway, operating in a planar environment. This, together with wheel odometers, an Inertial Measurement Unit (IMU), two Microsoft Kinects and a laptop comprise the backbone of the system, which can be divided into three parts: The localization and mapping part, which fundamentally is a SLAM (simultaneous localization and mapping) algorithm implemented using the registration technique Iterative Closest Point (ICP). Along with the map being in 3D, it also designed to handle the mapping of dynamic scenes, something absent from the standard SLAM design. The planning used by the system is twofold. First, the path planning - finding a path from the current position to a destination - and second, the target planning - determining where to go next given the current state of the map and the robot. The third part of the system is the control and collision systems, which while they have not received much focus, are very necessary for a fully autonomous system. Contributions made by this thesis include: The 3D map framework Octomap is extended to handle the mapping of dynamic scenes; A new method for target planning, based on image processing is presented; A calibration procedure for the robot is derived that gives a full six degree of freedom pose for each Kinect. Results show that our calibration procedure produces an accurate pose for each Kinect, which is crucial for a functioning system. The dynamic mapping is shown to outperform the standard occupancy grid in fundamental situations that arise when mapping dynamic scenes. Additionally, the results indicate that the target planning algorithm provides a fast and easy way to plan new target destinations. Finally, the entire system’s autonomous mapping capabilities are evaluated together, producing promising results. However, it also highlights some problems that limit the system’s performance such as the inaccuracy and short range of the Kinects or noise added and reinforced by the multiple subsystems / Detta exjobb beskriver delarna som krävs för att för bygga ett komplett system som autonomt kartlägger inomhusmiljöer i tre dimensioner. Robotplattformen är en Segway, som är kapabel att röra sig i ett plan. Segwayn, tillsammans med en tröghetssensor, två Microsoft Kinects och en bärbar dator utgör grunden till systemet, som kan delas i tre delar: En lokaliserings- och karteringsdel, som i grunden är en SLAM-algoritm (simultan lokalisering och kartläggning)  baserad på registreringsmetoden Iterative Closest Point (ICP). Kartan som byggs upp är i tre dimensioner och ska dessutom hantera kartläggningen av dynamiska miljöer, något som orginalforumleringen av SLAM problemet inte klarar av. En automatisk planeringsdel, som består av två delar. Dels ruttplanering som går ut på att hitta en väg från sin nuvarande position till det valda målet och dels målplanering som innebär att välja ett mål att åka till givet den nuvarande kartan och robotens nuvarande position. Systemets tredje del är regler- och kollisionssystemen. Dessa system har inte varit i fokus i detta arbete, men de är ändå högst nödvändiga för att ett autonomt system skall fungera. Detta examensarbete bidrar med följande: Octomap, ett ramverk för kartläggningen i 3D, har utökats för att hantera kartläggningen av dynamiska miljöer; En ny metod för målplanering, baserad på bildbehandling läggs fram; En kalibreringsprocedur för roboten är framtagen som ger den fullständiga posen i förhållande till roboten för varje Kinect. Resultaten visar att vår kalibreringsprocedur ger en nogrann pose for för varje Kinect, vilket är avgörande för att systemet ska fungera. Metoden för kartläggningen av dynamiska miljöer visas prestera bra i grundläggande situationer som uppstår vid kartläggning av dynamiska miljöer. Vidare visas att målplaneringsalgoritmen ger ett snabbt och enkelt sätt att planera mål att åka till. Slutligen utvärderas hela systemets autonoma kartläggningsförmåga, som ger lovande resultat. Dock lyfter resultat även fram problem som begränsar systemets prestanda, till exempel Kinectens onoggranhet och korta räckvidd samt brus som läggs till och förstärks av de olika subsystemen.