Fremen: Frequency map enhancement for long-term mobile robot autonomy in changing environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00311497" target="_blank" >RIV/68407700:21230/17:00311497 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7878680/" target="_blank" >http://ieeexplore.ieee.org/document/7878680/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TRO.2017.2665664" target="_blank" >10.1109/TRO.2017.2665664</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fremen: Frequency map enhancement for long-term mobile robot autonomy in changing environments

Popis výsledku v původním jazyce

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot’s long-term performance in dynamic environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model’s predictive capabilities improve mobile robot localisation and navigation in changing environments.

Název v anglickém jazyce

Fremen: Frequency map enhancement for long-term mobile robot autonomy in changing environments

Popis výsledku anglicky

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot’s long-term performance in dynamic environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model’s predictive capabilities improve mobile robot localisation and navigation in changing environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ17-27006Y" target="_blank" >GJ17-27006Y: Prostorově temporální representace pro dlouhodobou navigaci mobilních robotů</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Robotics

ISSN

1552-3098

e-ISSN

1941-0468

Svazek periodika

33

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

964-977

Kód UT WoS článku

000407395200015

EID výsledku v databázi Scopus

2-s2.0-85015635452