Human Localization in Robotized Warehouses based on Stereo Odometry and Ground-Marker Fusion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00351935" target="_blank" >RIV/68407700:21730/22:00351935 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.rcim.2021.102241" target="_blank" >https://doi.org/10.1016/j.rcim.2021.102241</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rcim.2021.102241" target="_blank" >10.1016/j.rcim.2021.102241</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Human Localization in Robotized Warehouses based on Stereo Odometry and Ground-Marker Fusion

Popis výsledku v původním jazyce

Modern logistic solutions for large warehouses consist of a fleet of robots that transfer goods, move racks, and perform other physically difficult and repetitive tasks. The shopfloor is usually enclosed with a safety fence and if a human needs to enter the warehouse all the robots are stopped, as opposed to only the ones in the most immediate vicinity of the human, thus significantly limiting the warehouse efficiency. To tackle this challenge, an integrated safety system is needed with human localization as one of its essential components. In this paper, we propose a novel human localization method for robotized warehouses that is based on a suite of wearable visual sensors installed on a vest worn by humans. The proposed method does not require any modifications of the warehouse environment and relies on the already existing infrastructure. Specifically, we estimate the human location by fusing stereo visual-inertial odometry data and distances to the known absolute poses of the detected ground-markers which robots use for their localization. Fusion is performed by building a pose graph, where we treat estimated human poses relative to markers as graph nodes and odometry estimates as graph edges. We conducted extensive laboratory and warehouse facility experiments, where we tested the reliability and accuracy of the proposed method and compared its performance to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results indicate that our method can track absolute position in real-time and has competitive accuracy with respect to ORB-SLAM2, while ensuring higher localization reliability when faced with structural changes in the environment. Furthermore, we provide publicly the experimental datasets to the research community.

Název v anglickém jazyce

Human Localization in Robotized Warehouses based on Stereo Odometry and Ground-Marker Fusion

Popis výsledku anglicky

Modern logistic solutions for large warehouses consist of a fleet of robots that transfer goods, move racks, and perform other physically difficult and repetitive tasks. The shopfloor is usually enclosed with a safety fence and if a human needs to enter the warehouse all the robots are stopped, as opposed to only the ones in the most immediate vicinity of the human, thus significantly limiting the warehouse efficiency. To tackle this challenge, an integrated safety system is needed with human localization as one of its essential components. In this paper, we propose a novel human localization method for robotized warehouses that is based on a suite of wearable visual sensors installed on a vest worn by humans. The proposed method does not require any modifications of the warehouse environment and relies on the already existing infrastructure. Specifically, we estimate the human location by fusing stereo visual-inertial odometry data and distances to the known absolute poses of the detected ground-markers which robots use for their localization. Fusion is performed by building a pose graph, where we treat estimated human poses relative to markers as graph nodes and odometry estimates as graph edges. We conducted extensive laboratory and warehouse facility experiments, where we tested the reliability and accuracy of the proposed method and compared its performance to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results indicate that our method can track absolute position in real-time and has competitive accuracy with respect to ORB-SLAM2, while ensuring higher localization reliability when faced with structural changes in the environment. Furthermore, we provide publicly the experimental datasets to the research community.

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

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Robotics and Computer-Integrated Manufacturing

ISSN

0736-5845

e-ISSN

1879-2537

Svazek periodika

73

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000704359300005

EID výsledku v databázi Scopus

2-s2.0-85114131314