Sökning: "Robust Localization"

Visar resultat 6 - 10 av 57 uppsatser innehållade orden Robust Localization.

  1. 6. Outlier Robustness in Server-Assisted Collaborative SLAM : Evaluating Outlier Impact and Improving Robustness

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

    Författare :José Miguel de Almeida Pedro; [2023]
    Nyckelord :SLAM; Robust Estimation; Multi-Device Algorithms; SLAM; Robust uppskattning; Algoritmer för flera enheter;

    Sammanfattning : In order to be able to perform many tasks, autonomous devices need to understand their environment and know where they are in this environment. Simultaneous Localisation and Mapping (SLAM) is a solution to this problem. LÄS MER

  3. 7. Robust visual SLAM with compressed image data : A study of ORB-SLAM3 performance under extreme image compression

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

    Författare :Guangzhi Wang; [2023]
    Nyckelord :ORB-SLAM3; compression; image feature; localization accuracy; internal parameter; hybrid compressing; brightness enhancement; ORB-SLAM3; kompression; bildfunktion; lokaliseringsnoggrannhet; intern parameter; hybridkomprimering; ljusstyrkeförstärkning;

    Sammanfattning : Offloading SLAM to the edge/cloud is now becoming an attractive option to greatly decrease device energy usage. The new SLAM solution involves compressing image data on the device before transmission, allowing a further decrease in the network bandwidth when performing SLAM at the edge/cloud. LÄS MER

  4. 8. The Interconnectivity Between SLAM and Autonomous Exploration : Investigation Through Integration

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

    Författare :Elliði Ívarsson; [2023]
    Nyckelord :SLAM; ORB-SLAM2; Autonomous exploration; UFOExplorer; Integration; SLAM; ORB-SLAM2; Autonom Utforskning; UFOExplorer; Integration;

    Sammanfattning : Two crucial functionalities of a fully autonomous robotic agent are localization and navigation. The problem of enabling an agent to localize itself in an unknown environment is an extensive and widely studied topic. One of the main areas of this topic focuses on Simultaneous Localization and Mapping (SLAM). LÄS MER

  5. 9. Image and RADAR fusion for autonomous vehicles

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

    Författare :Xavier de Gibert Duart; [2023]
    Nyckelord :Image; Camera; Computer Vision; Data Fusion; Sensors; 3D data processing; Point Clouds; Calibration; MATLAB and RADAR; RADAR; kamera; datorseende; datafusion; sensorer; 3D-databehandling; punktmoln; kalibrering; MATLAB Image;

    Sammanfattning : Robust detection, localization, and tracking of objects are essential for autonomous driving. Computer vision has largely driven development based on camera sensors in recent years, but 3D localization from images is still challenging. Sensors such as LiDAR or RADAR are used to compute depth; each having its own advantages and drawbacks. LÄS MER

  7. 10. Through the Blur with Deep Learning : A Comparative Study Assessing Robustness in Visual Odometry Techniques

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Alexander Berglund; [2023]
    Nyckelord :artificial intelligence; AI; machine learning; ML; deep learning; DL; computer vision; neural networks; NN; convolutional neural networks; CNN; visual odometry; VO; robustness; motion blur; AirForestry; localization; navigation; ego-motion; pose estimation; SLAM; DF-VO; DytanVO; ORB-SLAM3; artificiell intelligens; maskininlärning; datorseende;

    Sammanfattning : In this thesis, the robustness of deep learning techniques in the field of visual odometry is investigated, with a specific focus on the impact of motion blur. A comparative study is conducted, evaluating the performance of state-of-the-art deep convolutional neural network methods, namely DF-VO and DytanVO, against ORB-SLAM3, a well-established non-deep-learning technique for visual simultaneous localization and mapping. LÄS MER