|
|
Basic Shapes Detection in Point Clouds
Eldes, Pavol ; Materna, Zdeněk (referee) ; Španěl, Michal (advisor)
This thesis deals with the topic of simple geometric shapes detection from point-clouds with a special interest in the influence of point normals on speed and quality of produced results. Its main product is an application that demonstrates detection of planes, spheres and cylinders. Detection is done using the RANSAC paradigm that is modified in this thesis to allow it to work with multiple models simultaneously. Another modification presented in this thesis focuses on enforcing stricter candidate shapes selection conditions for detection models.
|
|
|
LiDAR-based Indoor Self Localization and Mapping
Minařík, Jakub ; Gábrlík, Petr (referee) ; Ligocki, Adam (advisor)
This thesis introduces the problem of simultaneous localization and mapping (SLAM) with focus on the use of a 3D LiDAR sensor. Firsly is written an introduction to SLAM itself and explained graph-based SLAM and dense map representation. The two most common point cloud alignment algorithms ICP and NDT are described. Then research of existing projects solving this problem is carried out. Described projects are all open-source and most of them support the ROS system. One of the described projects, Optimized SC-F-LOAM is explained in detail. Thesis describes it's odometry FLOAM and connection between it and graph optimization with loop closure. For loop closure is used descriptor ScanContext. Then it is presented design for implementing choosen project on offline and online datas from indoor. In last chapters is described proces of implementing and tuning project and at the end results of using said project in indoors are presented.
|
|
|
Vizualizace mračen bodů pomocí Unreal Enginu
Zejda, Martin
This bachelor thesis deals with the development of an application for the visualization of point clouds using virtual reality. It contains a detailed description of the development of the application in Unreal Engine 4 and connecting this engine to the virtual reality of Oculus Rift and HTC Vive. Development includes importing and rendering of point clouds, performance optimalization, application control and interaction, virtual reality set-up, and user interface.
|
|
|
Automatic Point Clouds Merging
Hörner, Jiří ; Obdržálek, David (advisor) ; Vodrážka, Jindřich (referee)
Multi-robot systems are an established research area with a growing number of applications. Efficient coordination in such systems usually requires knowledge of robot positions and the global map. This work presents a novel map-merging algorithm for merging 3D point cloud maps in multi-robot systems, which produces the global map and estimates robot positions. The algorithm is based on feature- matching transformation estimation with a novel descriptor matching scheme and works solely on point cloud maps without any additional auxiliary information. The algorithm can work with different SLAM approaches and sensor types and it is applicable in heterogeneous multi-robot systems. The map-merging algorithm has been evaluated on real-world datasets captured by both aerial and ground-based robots with a variety of stereo rig cameras and active RGB-D cameras. It has been evaluated in both indoor and outdoor environments. The proposed algorithm was implemented as a ROS package and it is currently distributed in the ROS distribution. To the best of my knowledge, it is the first ROS package for map-merging of 3D maps.
|
|
|
Basic Shapes Detection in Point Clouds
Eldes, Pavol ; Materna, Zdeněk (referee) ; Španěl, Michal (advisor)
This thesis deals with the topic of simple geometric shapes detection from point-clouds with a special interest in the influence of point normals on speed and quality of produced results. Its main product is an application that demonstrates detection of planes, spheres and cylinders. Detection is done using the RANSAC paradigm that is modified in this thesis to allow it to work with multiple models simultaneously. Another modification presented in this thesis focuses on enforcing stricter candidate shapes selection conditions for detection models.
|
guest ::
