Academic literature on the topic 'Simultánní lokalizace a mapování'

The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.

This work presents an overview of the simultaneous localisation and mapping (SLAM) problem in the mobile robotics. The Extended Kalman filter (EKF) based algorithm for localisation and mapping is proposed. For EKF algorithm the models of the skid steering mobile robot and the laser scanner are presented. The EKF algortihm is feature based algorithm, therefore the method for the landmark position determination was developed. This segmentation method is based on the clustering of the Radon transform space. Proposed SLAM algorithm was tested with real data measured with UTAR mobile platform. Achievments of the work are summarized in the conclusion of the proposed work and possible improvements of the components are suggested.

The Master thesis is focused on analyzing stereo camera ZED in the outdoor environment. There is compared ZEDfu visual odometry with commonly used methods like GPS or wheel odometry. Moreover, the thesis includes analyses of SLAM in the changeable outdoor environment, too. The simultaneous mapping and localization in RTAB-Map were processed separately with SIFT and BRISK descriptors. The aim of this master thesis is to analyze the behaviour ZED camera in the outdoor environment for future implementation in mobile robotics.

This master's thesis deals with the creation of a tool for the extraction of methylation level from transposon sequences. Transposons are DNA elements with ability to move or copy themselves and their activity is regulated by DNA methylation. Sequence methylation information is stored in the bisulfite data and their processing is done with parts of two existing tools in a combination with implemented modules. Created tool takes into consideration unique challenges brought in the methylation calling process by transposable elements and it's functionality is presented on a set of experiments with simulated and real data.

This paper describe design and implementation of SLAM algorithm for selflocalization and mapping in indoor environment using data from laser scanner. Design is focused on 2D variant of SLAM, but parts is purposely reliazed to be usable in 3D SLAM. This ability is demonstrated at the end of paper.

The dissertation is aimed at methods for simultaneous processing of various type sensor data into a virtual map. Conceptually the development was focused on algorithms for simultaneous localization and mapping. In theoretical part has the problem been addressed by the probabilistic approach. The practical part deals with a new approach called as partially collective mapping whose application leads to creation map in form of a set of simple geometrical entities which represents in piecewise manner a border between obstacles and free space.

This paper deals with the application of SLAM algorithms on experimental four wheel vehicle Car4. The first part shows the basic functioning of SLAM including a description of the extended Kalman filter, which is one of its main components. Then there is a brief list of software tools available to solve this problem in the environment of MATLAB and an overview of sensors used in this work. The second part presents methodology and results of the testing of individual sensors and their combinations to calculate odometry and scan the surrounding space. It also shows the process of applying SLAM algorithms on Car4 vehicle using the selected sensors and the results of testing of the entire system in practice.

Tato práce se zaměřuje na autonomní lokalizaci radiologických zdrojů v definované oblasti zájmu. Jejím cílem je vyvinout lokalizační strategie a platformu, na které je bude možné vyzkoušet. Platforma je sestavena z průzkumného robotu Orpheus-X3, scintilačních detektorů a přesného GNSS přijímače. Algoritmus pro vytváření mapy distribuce radiačního pole je rozšířen. Jsou představeny nové metody založené na směrové citlivosti navrhovaného detekčního systému. Počáteční průzkum oblasti zájmu je uskutečněn pomocí kružnicových trajektorií. Všechny algoritmy byly otestována jak simulacemi, tak reálnými experimenty. Dosažená přesnosti lokalizace je v řádu desítek centimetrů. Časová efektivita je použitím nových algoritmů zvýšena přibližně dvakrát až pětkrát. Jedním z~přínosů práce je vývoj modulárního systému, který může být přesunut a uzpůsoben na jiné platformy. Výhodou použitého systému je vysoký stupeň autonomie a bezpečnost pro lidského operátora.

This thesis concentrates on definition and solution of simultaneous localization and mapping using extended Kalman filter. It also shows use of subomptimal methods based on bounded region filtering and Monte Carlo methods.