Sökning: "odometri"

Visar resultat 1 - 5 av 21 uppsatser innehållade ordet odometri.

  1. 1. 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

  2. 2. Robotics Approach in Mobile Laser Scanning : Generation of Georeferenced Point-based Forest Models

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

    Författare :Tamas Faitli; [2023]
    Nyckelord :mobile laser scanning; rotating lidar; slam; real-time positioning; georeferencing; state estimation; lidar odometry; point-based forest model; forest harvester; forestry; mobil laserskanning; roterande lidar; slam; realtidspositionering; georeferens; tillståndsuppskattning; lidarodometri; punktbaserad skogsmodell; skogsskördare; skogsbruk;

    Sammanfattning : A mobile laser scanning (MLS) system equipped with a lidar, inertial navigation system and satellite positioning can be used to reconstruct georeferenced point-based models of the surveyed environments. Ideal reconstruction requires accurate trajectories that are challenging to obtain in forests. LÄS MER

  3. 3. Deep Visual Inertial-Aided Feature Extraction Network for Visual Odometry : Deep Neural Network training scheme to fuse visual and inertial information for feature extraction

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

    Författare :Franco Serra; [2022]
    Nyckelord :Feature extraction network; Visual Odometry; IMU; Neural Network; Pose estimation; Feature extraction; Visuell Odometri; IMU; Neuralt nätverk; Poseuppskattning;

    Sammanfattning : Feature extraction is an essential part of the Visual Odometry problem. In recent years, with the rise of Neural Networks, the problem has shifted from a more classical to a deep learning approach. This thesis presents a fine-tuned feature extraction network trained on pose estimation as a proxy task. LÄS MER

  4. 4. Deep Monocular Visual Odometry for fixed-winged Aircraft : Exploring Deep-VO designed for ground use in a high altitude aerial environment

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

    Författare :Oliver Öhrstam Lindström; [2022]
    Nyckelord :Deep-VO; Aviation; Fixed-Wing; Aircraft; Deep Learning; Deep-VO; Hög Höjd; Flygplan; Deep Learning;

    Sammanfattning : In aviation, safety is a big concern. Knowing the position of an aircraft at all times is of high importance. Today most aircraft utilize Global Navigation Satellite Systems (GNSS) for localization and precision navigation because of the small position error which do not increase over time. LÄS MER

  5. 5. RGB-D Deep Learning keypoints and descriptors extraction Network for feature-based Visual Odometry systems

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

    Författare :Federico Bennasciutti; [2022]
    Nyckelord :DeepLearning; Visual Odometry; Computer Vision; RGB-D Camera; Feature Extraction; Interest Point Extraction; Djupinlärning; Visuell Odometri; Datorseende; RGB-D-kamera; Nyckelpunkter; Detektion;

    Sammanfattning : Feature extractors in Visual Odometry pipelines rarely exploit depth signals, even though depth sensors and RGB-D cameras are commonly used in later stages of Visual Odometry systems. Nonetheless, depth sensors from RGB-D cameras function even with no external light and can provide feature extractors with additional structural information otherwise invisible in RGB images. LÄS MER