Sökning: "Monocular Camera"

Visar resultat 1 - 5 av 59 uppsatser innehållade orden Monocular Camera.

  1. 1. Deep Learning-Based Depth Estimation Models with Monocular SLAM : Impacts of Pure Rotational Movements on Scale Drift and Robustness

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Daniel Bladh; [2023]
    Nyckelord :Deep Learning; Computer Vision; Monocular; SLAM; Depth Estimation;

    Sammanfattning : This thesis explores the integration of deep learning-based depth estimation models with the ORB-SLAM3 framework to address challenges in monocular Simultaneous Localization and Mapping (SLAM), particularly focusing on pure rotational movements. The study investigates the viability of using pre-trained generic depth estimation networks, and hybrid combinations of these networks, to replace traditional depth sensors and improve scale accuracy in SLAM systems. LÄS MER

  3. 2. Visual-Inertial SLAM Using a Monocular Camera and Detailed Map Data

    Master-uppsats, Linköpings universitet/Reglerteknik

    Författare :Viktor Ekström; Ludvig Berglund; [2023]
    Nyckelord :SLAM; localisation; monocular camera; GTSAM; factor graphs; iSAM2;

    Sammanfattning : The most commonly used localisation methods, such as GPS, rely on external signals to generate an estimate of the location. There is a need of systems which are independent of external signals in order to increase the robustness of the localisation capabilities. 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. Rolling shutter in feature-based Visual-SLAM : Robustness through rectification in a wearable and monocular context

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

    Författare :Caspar Norée Palm; [2023]
    Nyckelord :SLAM; rolling; shutter; rectification; ORB-SLAM3; monocular; camera; compensation; visual; odometry; simultaneous; localization; mapping;

    Sammanfattning : This thesis analyzes the impact of and implements compensation for rolling shutter distortions in the state-of-the-art feature-based visual SLAM system ORB-SLAM3. The compensation method involves rectifying the detected features, and the evaluation was conducted on the "Rolling-Shutter Visual-Inertial Odometry Dataset" from TUM, which comprises of ten sequences recorded with side-by-side synchronized global and rolling shutter cameras in a single room. LÄS MER

  7. 5. Skyline Delineation for Localization in Occluded Environments : Improved Skyline Delineation using Environmental Context from Deep Learning-based Semantic Segmentation

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

    Författare :Kyle William Coble; [2023]
    Nyckelord :Skyline delineation; Skyline detection; Semantic segmentation; Terrain based navigation; Digital elevation models; Uncrewed surface vessel; Planetary exploration robots; Horisont avgränsning; Horisont upptäckt; Semantisk segmentering; Terrängbaserad navigering; Digitala höjdmodeller; Obemannat ytfartyg; Planetariska utforskningsrobotar;

    Sammanfattning : This thesis addresses the problem of improving the delineation of skylines, also referred to as skyline detection, in occluded and challenging environments where existing skyline delineation methods may struggle or fail. Delineated skylines can be used in monocular camera localization methods by comparing delineated skylines to digital elevation model data to estimate a position based on known terrain. LÄS MER