Non odometry SLAM and Effect of Feature Space Parametrization on its Covariance Convergence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120610" target="_blank" >RIV/00216305:26220/16:PU120610 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ifacol.2016.12.024" target="_blank" >http://dx.doi.org/10.1016/j.ifacol.2016.12.024</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2016.12.024" target="_blank" >10.1016/j.ifacol.2016.12.024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non odometry SLAM and Effect of Feature Space Parametrization on its Covariance Convergence

Popis výsledku v původním jazyce

This paper is aimed at non-odometry feature-based variant of SLAM (Simultaneous Localisation and Mapping) algorithm. The main focus has been given to processing of linearly constraint map landmarks observations. The observation model was simplified to linear and with this assumption is given derivation of three different variants of applicable SLAM algorithm – first variant which doesn’t take into consideration any map feature constraints, second variant which optimally process data under linear constraints assumption and third reduced variant which is designed to slightly suboptimal processing with significantly lower computational complexity. The performance of described methods was tested on synthetic data and results of simulations are presented at the end of this paper.

Název v anglickém jazyce

Non odometry SLAM and Effect of Feature Space Parametrization on its Covariance Convergence

Popis výsledku anglicky

This paper is aimed at non-odometry feature-based variant of SLAM (Simultaneous Localisation and Mapping) algorithm. The main focus has been given to processing of linearly constraint map landmarks observations. The observation model was simplified to linear and with this assumption is given derivation of three different variants of applicable SLAM algorithm – first variant which doesn’t take into consideration any map feature constraints, second variant which optimally process data under linear constraints assumption and third reduced variant which is designed to slightly suboptimal processing with significantly lower computational complexity. The performance of described methods was tested on synthetic data and results of simulations are presented at the end of this paper.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

14th IFAC Conference on Programmable Devices and Embedded Systems - PDeS 2016

ISBN

—

ISSN

2405-8963

e-ISSN

—

Počet stran výsledku

6

Strana od-do

357-362

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Brno/Lednice

Datum konání akce

5. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000401255800024