Sökning: "Point Cloud Classification"

Visar resultat 1 - 5 av 30 uppsatser innehållade orden Point Cloud Classification.

1. 1. Performance metrics and velocity influence for point cloud registration in autonomous vehicles

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Óscar Poveda Ruiz; [2023]
   Nyckelord :autonomous vehicle; localization; registration; metrics; error; classification; estimation; autonomt fordon; lokalisering; registrering; mätvärden; fel; klassificering; uppskattning;

   Sammanfattning : Autonomous vehicles are currently under study and one of the critical parts is the localization of the vehicle in the environment. Different localization methods have been studied over the years, such as the GPS sensor, commonly fused with other sensors such as the IMU. LÄS MER

3. 2. A requirements engineering approach in the development of an AI-based classification system for road markings in autonomous driving : a case study

   Master-uppsats, Blekinge Tekniska Högskola/Institutionen för programvaruteknik

   Författare :Srija Sunkara; [2023]
   Nyckelord :Requirements Engineering; Machine Learning; Goal-Oriented Requirements Engineering; Autonomous Driving; Point Cloud Classification;

   Sammanfattning : Background: Requirements engineering (RE) is the process of identifying, defining, documenting, and validating requirements. However, RE approaches are usually not applied to AI-based systems due to their ambiguity and is still a growing subject. LÄS MER

4. 3. Using a Deep Generative Model to Generate and Manipulate 3D Object Representation

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

   Författare :Yu Hu; [2023]
   Nyckelord :Neural networks; point cloud; 3D shape generation; 3D shape manipulation; classification; Neurala nätverk; punktmoln; generering av 3D-former; manipulation av 3Dformer; klassificering;

   Sammanfattning : The increasing importance of 3D data in various domains, such as computer vision, robotics, medical analysis, augmented reality, and virtual reality, has gained giant research interest in generating 3D data using deep generative models. The challenging problem is how to build generative models to synthesize diverse and realistic 3D objects representations, while having controllability for manipulating the shape attributes of 3D objects. LÄS MER

5. 4. Automatic processing of LiDAR point cloud data captured by drones

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Leon Li Persson; [2023]
   Nyckelord :machine learning; supervised learning; random forest; point cloud segmentation;

   Sammanfattning : As automation is on the rise in the world at large, the ability to automatically differentiate objects in datasets via machine learning is of growing interest. This report details an experimental evaluation of supervised learning on point cloud data using random forest with varying setups. LÄS MER

7. 5. Feature-Aware Point Transformer for Point Cloud Alignment Classification : Pose your pose to FACT

   Master-uppsats, Linköpings universitet/Institutionen för systemteknik

   Författare :Ludvig Dillén; [2023]
   Nyckelord :point cloud alignment; point cloud alignment classification; alignment validation; point cloud; map validation; map quality; misalignment recognition; alignment quality; point transformer; Sinkhorn divergence; differential entropy; point cloud co-visibility; point cloud visibility; SLAM; point cloud registration; point cloud density;

   Sammanfattning : As the demand for 3D maps from LIDAR scanners increases, delivering high-quality maps becomes critical. One way to ensure the quality of such maps is through point cloud alignment classification, which aims to classify the alignment error between two registered point clouds. LÄS MER