Sökning: "Robust Localization"

Visar resultat 1 - 5 av 57 uppsatser innehållade orden Robust Localization.

  1. 1. Automatic Semantic Segmentation of Indoor Datasets

    Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Sai Swaroop Rachakonda; [2024]
    Nyckelord :Semantic Segmentation; Annotation; SLAM; Indoor datasets; YOLO V8; DETIC; Segment Anything Model.;

    Sammanfattning : Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. LÄS MER

  3. 2. Experiments with Visual Odometry for Hydrobatic Autonomous Underwater Vehicles

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Somnath Balaji Suresh Kumar; [2023]
    Nyckelord :Hydrobatic AUVs; VO; Stonefish; ORB-SLAM2; VISO2; Hydrobatiska AUV:er; VO; Stonefish; ORB-SLAM2; VISO2;

    Sammanfattning : Hydrobatic Autonomous Underwater Vehicles (AUVs) are underactuated robots that can perform agile maneuvers in challenging underwater environments with high efficiency in speed and range. The challenge lies in localizing and navigating these AUVs particularly for performing manipulation tasks because common sensors such as GPS become very unreliable underwater due to their poor accuracy. LÄS MER

  4. 3. ROS-based implementation of a model car with a LiDAR and camera setup

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och system

    Författare :Marcus Nises; [2023]
    Nyckelord :LiDAR; ROS; stereoscopic camera; SLAM; Linux; rplidar a1; raspberry pi;

    Sammanfattning : The aim of this project is to implement a Radio Controlled (RC) car with a Light Detection and Ranging (LiDAR) sensor and a stereoscopic camera setup based on the Robot Operating System (ROS) to conduct Simultaneous Localization and Mapping (SLAM). The LiDAR sensor used is a 2D LiDAR, RPlidar A1, and the stereoscopic camera setup is made of two monocular cameras, Raspberry Pi Camera v2. LÄS MER

  5. 4. Robust Non-Linear State Estimation for Underwater Acoustic Localization : Expanding on Gaussian Mixture Methods

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Diogo Antunes; [2023]
    Nyckelord :Robust state estimation; Underwater localization; Target tracking; Gaussian mixture; AUV; Estimação robusta de estado; Localização subaquática; Rastreamento de alvos; Mistura Gaussiana; AUV; Robust tillståndsuppskattning; Undervattenslokalisering; Målspårning; Gaussisk blandning; AUV;

    Sammanfattning : Robust state estimation solutions must deal with faulty measurements, called outliers, and unknown data associations, which lead to multiple feasible hypotheses. Take, for instance, the scenario of tracking two indistinguishable targets based on position measurements, where each measurement could refer to either of the targets or even be a faulty reading. LÄS MER

  7. 5. Event-Based Visual SLAM : An Explorative Approach

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och system

    Författare :Johan Rideg; [2023]
    Nyckelord :event camera; neuromorphic; SLAM; visual odometry;

    Sammanfattning : Simultaneous Localization And Mapping (SLAM) is an important topic within the field of roboticsaiming to localize an agent in a unknown or partially known environment while simultaneouslymapping the environment. The ability to perform robust SLAM is especially important inhazardous environments such as natural disasters, firefighting and space exploration wherehuman exploration may be too dangerous or impractical. LÄS MER