Essays about: "Punktmoln"

Showing result 6 - 10 of 66 essays containing the word Punktmoln.

  1. 6. Domain Adaptation Of Front View Synthetic Point Clouds Using GANs For Autonomous Driving

    University essay from KTH/Väg- och spårfordon samt konceptuell fordonsdesign

    Author : Friedemann Kleinsteuber; [2023]
    Keywords : LiDAR; Domain Adaptation; GAN; CycleGAN; Simulation; LiDAR; Domänadaption; GAN; CycleGAN; Simulation;

    Abstract : The perception of the environment is one of the main enablers of Autonomous Driving and is driven by Cameras, RADAR, and LiDAR sensors. Deep Learning algorithms used in perception need a vast amount of labeled, high-quality data which is costly to obtain for LiDAR sensors. READ MORE

  3. 7. Image and RADAR fusion for autonomous vehicles

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Xavier de Gibert Duart; [2023]
    Keywords : Image; Camera; Computer Vision; Data Fusion; Sensors; 3D data processing; Point Clouds; Calibration; MATLAB and RADAR; RADAR; kamera; datorseende; datafusion; sensorer; 3D-databehandling; punktmoln; kalibrering; MATLAB Image;

    Abstract : Robust detection, localization, and tracking of objects are essential for autonomous driving. Computer vision has largely driven development based on camera sensors in recent years, but 3D localization from images is still challenging. Sensors such as LiDAR or RADAR are used to compute depth; each having its own advantages and drawbacks. READ MORE

  4. 8. Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Isabella Luppi; [2023]
    Keywords : Distributed Sensor Networks; Point Cloud Processing; Bounding Box Fitting; Trajectory Tracking; Distributed Estimation; Predictive Estimation; Edge-Computing; Reti di Sensori Distribuiti; Elaborazione di Nuvole di Punti; Riquadri di Delimitazione; Tracciamento della Traiettoria; Stima Distribuita; Stima Predittiva; Calcolo Distribuito.; Distribuerade Sensornätverk; Bearbetning av Punktmoln; Anpassning av Begränsningsruta; Trajektorieuppföljning; Distribuerad Uppskattning; Prediktiv Uppskattning; Edge-datorbehandling;

    Abstract : This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. READ MORE

  5. 9. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mattias Hansson; [2023]
    Keywords : Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

    Abstract : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. READ MORE

  6. 10. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Shams Methnani; [2023]
    Keywords : ;

    Abstract : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. READ MORE