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81	Through the Blur with Deep Learning : A Comparative Study Assessing Robustness in Visual Odometry Techniques Berglund, Alexander January 2023 (has links) 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. The objective is to quantitatively assess the performance of these models as a function of motion blur. The evaluation is carried out on a custom synthetic dataset, which simulates a camera navigating through a forest environment. The dataset includes trajectories with varying degrees of motion blur, caused by camera translation, and optionally, pitch and yaw rotational noise. The results demonstrate that deep learning-based methods maintained robust performance despite the challenging conditions presented in the test data, while excessive blur lead to tracking failures in the geometric model. This suggests that the ability of deep neural network architectures to automatically learn hierarchical feature representations and capture complex, abstract features may enhance the robustness of deep learning-based visual odometry techniques in challenging conditions, compared to their geometric counterparts. 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 Computer Sciences Datavetenskap (datalogi)
82	Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game / Användbarhet i ett handgestbaserat AR-spel Lam, Benny, Nilsson, Jakob January 2019 (has links) The dissemination of new innovative technology requires feasibility and simplicity. The problem with marker-based augmented reality is similar to glove-based hand gesture recognition: they both require an additional component to function. This thesis investigates the possibility of combining markerless augmented reality together with appearance-based hand gesture recognition by implementing a game with good user experience. The methods employed in this research consist of a game implementation and a pre-study meant for measuring interactive accuracy and precision, and for deciding upon which gestures should be utilized in the game. A test environment was realized in Unity using ARKit and Manomotion SDK. Similarly, the implementation of the game used the same development tools. However, Blender was used for creating the 3D models. The results from 15 testers showed that the pinching gesture was the most favorable one. The game was evaluated with a System Usability Scale (SUS) and received a score of 70.77 among 12 game testers, which indicates that the augmented reality game, which interaction method is solely based on bare-hands, can be quite enjoyable. AR Hand gestures Bare-hand interaction HGR Augmented reality usability user experience Manomotion ARKit Visual Odometry SLAM VO VIO 3D gestural interaction gesture recognition gesture tracking augmented environments Förstärkt verklighet 3D gestinteraktion gestigenkänning visuell odometri Manomotion ARKit Engineering and Technology Teknik och teknologier Interaction Technologies Interaktionsteknik Design Design Human Computer Interaction Probability Theory and Statistics Sannolikhetsteori och statistik Computer Systems Datorsystem Software Engineering Programvaruteknik

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

Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game / Användbarhet i ett handgestbaserat AR-spel