General concepts of multi-sensor data-fusion based SLAM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU136667" target="_blank" >RIV/00216305:26220/20:PU136667 - isvavai.cz</a>

Výsledek na webu

<a href="http://ijra.iaescore.com/index.php/IJRA/article/view/20258/12906" target="_blank" >http://ijra.iaescore.com/index.php/IJRA/article/view/20258/12906</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11591/ijra.v9i2.pp63-72" target="_blank" >10.11591/ijra.v9i2.pp63-72</a>

Jazyk výsledku

angličtina

Název v původním jazyce

General concepts of multi-sensor data-fusion based SLAM

Popis výsledku v původním jazyce

This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specifically on processing of data from a heterogeneous set of sensors concurrently. Sensors are considered to be different in a sense of measured physical quantity and so the problem of effective data-fusion is discussed. A special extension of the standard probabilistic approach to SLAM algorithms is presented. This extension is composed of two parts. Firstly is presented general perspective multiple-sensors based SLAM and then thee archetypical special cases are discuses. One archetype provisionally designated as ”partially collective mapping” has been analyzed also in a practical perspective because it implies a promising options for implicit map-level data-fusion.

Název v anglickém jazyce

General concepts of multi-sensor data-fusion based SLAM

Popis výsledku anglicky

This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specifically on processing of data from a heterogeneous set of sensors concurrently. Sensors are considered to be different in a sense of measured physical quantity and so the problem of effective data-fusion is discussed. A special extension of the standard probabilistic approach to SLAM algorithms is presented. This extension is composed of two parts. Firstly is presented general perspective multiple-sensors based SLAM and then thee archetypical special cases are discuses. One archetype provisionally designated as ”partially collective mapping” has been analyzed also in a practical perspective because it implies a promising options for implicit map-level data-fusion.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/TN01000024" target="_blank" >TN01000024: Národní centrum kompetence - Kybernetika a umělá inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Robotics and Automation (IJRA)

ISSN

2089-4856

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

ID - Indonéská republika

Počet stran výsledku

10

Strana od-do

63-72

Kód UT WoS článku

000000000000001

EID výsledku v databázi Scopus

—