A Precise Teach and Repeat Visual Navigation System Based on the Convergence Theorem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00318024" target="_blank" >RIV/68407700:21230/17:00318024 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Precise Teach and Repeat Visual Navigation System Based on the Convergence Theorem

Popis výsledku v původním jazyce

We present a simple teach-and-repeat visual navigation method robust to appearance changes induced by varying illumination and naturally-occurring environment changes. The method is computationally efficient, it does not require camera calibration and it can learn and autonomously traverse arbitrarily-shaped paths. During the teaching phase, where the robot is driven by a human operator, the robot stores its velocities and image features visible from its on-board camera. During autonomous navigation, the method does not perform explicit robot localisation in the 2d/3d space but it simply replays the velocities that it learned during a teaching phase, while correcting its heading relatively to the path based on its camera data. The experiments performed indicate that the proposed navigation system corrects position errors of the robot as it moves along the path. Therefore, the robot can repeatedly drive along the desired path, which was previously taught by the human operator. The presented system, which is based on a method that won the RoboTour challenge in 2009 and 2008, is provided as open source at www.github.com/gestom/ stroll_bearnav.

Název v anglickém jazyce

A Precise Teach and Repeat Visual Navigation System Based on the Convergence Theorem

Popis výsledku anglicky

We present a simple teach-and-repeat visual navigation method robust to appearance changes induced by varying illumination and naturally-occurring environment changes. The method is computationally efficient, it does not require camera calibration and it can learn and autonomously traverse arbitrarily-shaped paths. During the teaching phase, where the robot is driven by a human operator, the robot stores its velocities and image features visible from its on-board camera. During autonomous navigation, the method does not perform explicit robot localisation in the 2d/3d space but it simply replays the velocities that it learned during a teaching phase, while correcting its heading relatively to the path based on its camera data. The experiments performed indicate that the proposed navigation system corrects position errors of the robot as it moves along the path. Therefore, the robot can repeatedly drive along the desired path, which was previously taught by the human operator. The presented system, which is based on a method that won the RoboTour challenge in 2009 and 2008, is provided as open source at www.github.com/gestom/ stroll_bearnav.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

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