FreMEn: Frequency Map Enhancement for Long-Term Autonomy of Mobile Robots

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00317553" target="_blank" >RIV/68407700:21230/17:00317553 - isvavai.cz</a>

Výsledek na webu

<a href="https://cloudslam.fer.hr/cloudslam/workshop/speakers" target="_blank" >https://cloudslam.fer.hr/cloudslam/workshop/speakers</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

FreMEn: Frequency Map Enhancement for Long-Term Autonomy of Mobile Robots

Popis výsledku v původním jazyce

While robotic mapping of static environments has been widely studied, life-long mapping in non-stationary environments is still an open problem. We present an approach for long-term representation of natural environments, where many of the observed changes are caused by pseudo-periodic factors, such as seasonal variations, or humans performing their daily chores. Rather than using a fixed probability value, our method models the uncertainty of the elementary environment states by their persistence and frequency spectra. This allows to integrate sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient models that reflect the recurring patterns of activity in the environment. The frequency-enhanced spatio-temporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of weeks to years, we demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition and allows for life-long spatio-temporal exploration.

Název v anglickém jazyce

FreMEn: Frequency Map Enhancement for Long-Term Autonomy of Mobile Robots

Popis výsledku anglicky

While robotic mapping of static environments has been widely studied, life-long mapping in non-stationary environments is still an open problem. We present an approach for long-term representation of natural environments, where many of the observed changes are caused by pseudo-periodic factors, such as seasonal variations, or humans performing their daily chores. Rather than using a fixed probability value, our method models the uncertainty of the elementary environment states by their persistence and frequency spectra. This allows to integrate sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient models that reflect the recurring patterns of activity in the environment. The frequency-enhanced spatio-temporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of weeks to years, we demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition and allows for life-long spatio-temporal exploration.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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