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Visar resultat 1 - 5 av 83 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Mutual Enhancement of Environment Recognition and Semantic Segmentation in Indoor Environment

    Master-uppsats,

    Författare :Venkata Vamsi Challa; [2024]
    Nyckelord :Semantic Segmentation; Scene Classification; Environment Recognition; Machine Learning; Deep Learning; Image Classification; Vision Transformers; SAM Segment Anything Model ; Image Segmentation; Contour-aware semantic segmentation;

    Sammanfattning : Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. LÄS MER

  3. 2. Movement Estimation with SLAM through Multimodal Sensor Fusion

    Master-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakulteten

    Författare :Jimmy Cedervall Lamin; [2024]
    Nyckelord :slam; discrete-slam; continuous-slam; synchronous; asynchronous; computer vision; BRISK; opencv; ceres; visual; inertial; sensor fusion; multimodal; Simultaneous Localization and Mapping; time offset; pose estimation; quaternions; movement estimation;

    Sammanfattning : In the field of robotics and self-navigation, Simultaneous Localization and Mapping (SLAM) is a technique crucial for estimating poses while concurrently creating a map of the environment. Robotics applications often rely on various sensors for pose estimation, including cameras, inertial measurement units (IMUs), and more. LÄS MER

  4. 3. Robust light source detection for AUV docking

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

    Författare :Joar Edlund; [2023]
    Nyckelord :Autonomous Underwater Vehicle; Docking; Detection; Poseestimation; Visual Navigation; Autonoma Undervattensfordon; Dockning; Detektering; Positionsestimering; Visuell Navigation;

    Sammanfattning : For Autonomous Underwater Vehicles (AUVs) to be able to conduct longterm surveys, the ability to return to a docking station for maintenance and recharging is crucial. A dynamic docking system where a slowly moving submarine acts as the docking station provides increased hydrodynamic control and reduces the impact of environmental disturbances. LÄS MER

  5. 4. Autonomous Navigation in Partially-Known Environment using Nano Drones with AI-based Obstacle Avoidance : A Vision-based Reactive Planning Approach for Autonomous Navigation of Nano Drones

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

    Författare :Mattia Sartori; [2023]
    Nyckelord :Nano Drones; Obstacle Avoidance; Autonomous Exploration; Autonomous Surveillance; Resource-Constrained Drones; Safe Navigation; Reactive Planning; Vision-based Navigation; Nanodrönare; Undvikande av Hinder; Autonom Utforskning; Autonom Övervakning; Resursbegränsade Drönare; Säker Navigering; Reaktiv Planering; Visionsbaserad Navigering;

    Sammanfattning : The adoption of small-size Unmanned Aerial Vehicles (UAVs) in the commercial and professional sectors is rapidly growing. The miniaturisation of sensors and processors, the advancements in connected edge intelligence and the exponential interest in Artificial Intelligence (AI) are boosting the affirmation of autonomous nano-size drones in the Internet of Things (IoT) ecosystem. LÄS MER

  7. 5. Velocity Obstacle method adapted for Dynamic Window Approach

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

    Författare :Florian Coissac; [2023]
    Nyckelord :Autonomous navigation; Local planning; Dynamic obstacle avoidance; ROS; Autonom navigering; Lokal planering; Dynamiskt undvikande av hinder; ROS;

    Sammanfattning : This thesis project is part of an internship at Visual Behavior. The company aims at producing computer vision models for robotics, helping the machine to better understand the world through the camera eye. The image holds many features that deep learning models are able to extract: navigable area, depth inference and object detection. LÄS MER