Essays about: "Lidar point cloud"

Showing result 1 - 5 of 66 essays containing the words Lidar point cloud.

  1. 1. Enhancement of a Power Line Information System by Combining BIM and LiDAR Data

    University essay from KTH/Lantmäteri – fastighetsvetenskap och geodesi

    Author : Daniel Wollberg; [2024]
    Keywords : GIS; BIM; FME; CloudCompare; GIS; BIM; FME; CloudCompare;

    Abstract : With the great ongoing energy transition in Sweden, Svenska Kraftnät (SVK) sees a huge need for investment in the Swedish transmission network and supporting IT- systems.  SVK has a great amount of collected laser data over the electric power transmission network however this data does not contain any semantic attribution that can be analyzed on broader information systems. READ MORE

  3. 2. Point Cloud Registration using both Machine Learning and Non-learning Methods : with Data from a Photon-counting LIDAR Sensor

    University essay from Linköpings universitet/Datorseende

    Author : Maja Boström; [2023]
    Keywords : Point Cloud Registration; Machine Learning; Photon-counting LIDAR; Iterative Closest Point;

    Abstract : Point Cloud Registration with data measured from a photon-counting LIDAR sensor from a large distance (500 m - 1.5 km) is an expanding field. Data measuredfrom far is sparse and have low detail, which can make the registration processdifficult, and registering this type of data is fairly unexplored. READ MORE

  4. 3. LiDAR Perception in a Virtual Environment Using Deep Learning : A comparative study of state-of-the-art 3D object detection models on synthetic data

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

    Author : Samuel Skoog; [2023]
    Keywords : Object Detection; LiDAR; CARLA; Deep Learning; Autonomous Vehicles; Objektdetektering; LiDAR; CARLA; Djupinlärning; Autonoma fordon;

    Abstract : Perceiving the environment is a crucial aspect of autonomous vehicles. To plan the route, the autonomous vehicle needs to be able to detect objects such as cars and pedestrians. This is possible through 3D object detection. However, labeling this type of data is time-consuming. READ MORE

  5. 4. Clustering on groups for human tracking with 3D LiDAR

    University essay from Umeå universitet/Institutionen för datavetenskap

    Author : Simon Utterström; [2023]
    Keywords : Computer Vision; Computer Science; AI; Machine Learning; clustering; Kernel Density Clustering; tracking; LiDAR; 3D LiDAR; tracking; human; pedestrian; real time; Datavetenskap; Dataseende; clustering; SLR; CVC; KDEG; KDE; Kernel Density Clustering; HDBSCAN; DBSCAN; LiDAR; point cloud; tracking; human; pedestrian;

    Abstract : 3D LiDAR people detection and tracking applications rely on extracting individual people from the point cloud for reliable tracking. A recurring problem for these applications is under-segmentation caused by people standing close or interacting with each other, which in turn causes the system to lose tracking. READ MORE

  6. 5. 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