Sökning: "Semantic SLAM"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Semantic SLAM.

  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. Instance Segmentation on depth images using Swin Transformer for improved accuracy on indoor images

    Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

    Författare :Alfred Hagberg; Mustaf Abdullahi Musse; [2022]
    Nyckelord :Instance Segmentation; segmentation; deep learning; semantic segmentation; swin transformer; mask rcnn; rcnn; cascade mask rcnn; slam; simultaneous localization and mapping; object detection; COCO; NYU dataset; vision transformer;

    Sammanfattning : The Simultaneous Localisation And Mapping (SLAM) problem is an open fundamental problem in autonomous mobile robotics. One of the latest most researched techniques used to enhance the SLAM methods is instance segmentation. LÄS MER

  4. 3. The V-SLAM Hurdler : A Faster V-SLAM System using Online Semantic Dynamic-and-Hardness-aware Approximation

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

    Författare :Liu Mingxuan; [2022]
    Nyckelord :Approximate Computing; Deep Learning; Dynamic Environments; Object Detection; Online Controller; Semantic SLAM; Ungefärlig Beräkning; Djup Lärning; Dynamiska miljöer; Objektdetektion; Online Kontroller; Semantisk SLAM;

    Sammanfattning : Visual Simultaneous Localization And Mapping (V-SLAM) and object detection algorithms are two critical prerequisites for modern XR applications. V-SLAM allows XR devices to geometrically map the environment and localize itself within the environment, simultaneously. LÄS MER

  5. 4. Towards Visual-Inertial SLAM for Dynamic Environments Using Instance Segmentation and Dense Optical Flow

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

    Författare :Luis Alejandro Sarmiento Gonzalez; [2021]
    Nyckelord :Semantic SLAM; Stereo Vision; VisualInertial SLAM; Motion likelihood; Stereo disparity; Dense optical flow; Dynamic objects.; Semantisk SLAM; Stereo Vision; Visual-Inertial SLAM; Sannolikhet för rörelse; Stereoskillnader; Tätt optiskt flöde; Dynamiska objekt.;

    Sammanfattning : Dynamic environments pose an open problem for the performance of visual SLAM systems in real-life scenarios. Such environments involve dynamic objects that can cause pose estimation errors. LÄS MER

  7. 5. Semantic Scene Segmentation using RGB-D & LRF fusion

    Master-uppsats, Högskolan i Halmstad/CAISR Centrum för tillämpade intelligenta system (IS-lab)

    Författare :Harald Lilja; [2020]
    Nyckelord :RGB-D; LiDAR; CNN; deep multimodal fusion; robotics; autonomous vehicles;

    Sammanfattning : In the field of robotics and autonomous vehicles, the use of RGB-D data and LiDAR sensors is a popular practice for applications such as SLAM[14], object classification[19] and scene understanding[5]. This thesis explores the problem of semantic segmentation using deep multimodal fusion of LRF and depth data. LÄS MER