Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator

Seungyoun Shin; Joon Hyung Lee; Junhyug Noh; Sungjoon Choi

doi:10.1007/978-3-031-47634-1_2

Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator

Seungyoun Shin, Joon Hyung Lee, Junhyug Noh, Sungjoon Choi

Department of Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Indoor robots are becoming increasingly prevalent across a range of sectors, but the challenge of navigating multi-level structures through elevators remains largely uncharted. For a robot to operate successfully, it’s pivotal to have an accurate perception of elevator states. This paper presents a robust robotic system, tailored to interact adeptly with elevators by discerning their status, actuating buttons, and boarding seamlessly. Given the inherent issues of class imbalance and limited data, we utilize the YOLOv7 model and adopt specific strategies to counteract the potential decline in object detection performance. Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, offering a promising approach to improve indoor robotic navigation systems.

Original language	English
Title of host publication	Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings
Editors	Huimin Lu, Michael Blumenstein, Sung-Bae Cho, Cheng-Lin Liu, Yasushi Yagi, Tohru Kamiya
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	15-28
Number of pages	14
ISBN (Print)	9783031476334
DOIs	https://doi.org/10.1007/978-3-031-47634-1_2
State	Published - 2023
Event	7th Asian Conference on Pattern Recognition, ACPR 2023 - Kitakyushu, Japan Duration: 5 Nov 2023 → 8 Nov 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14406 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th Asian Conference on Pattern Recognition, ACPR 2023
Country/Territory	Japan
City	Kitakyushu
Period	5/11/23 → 8/11/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Class imbalance in detection methods
Mobile manipulator
Object detection

Access to Document

10.1007/978-3-031-47634-1_2

Cite this

Shin, S., Lee, J. H., Noh, J., & Choi, S. (2023). Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator. In H. Lu, M. Blumenstein, S.-B. Cho, C.-L. Liu, Y. Yagi, & T. Kamiya (Eds.), Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings (pp. 15-28). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14406 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-47634-1_2

Shin, Seungyoun ; Lee, Joon Hyung ; Noh, Junhyug et al. / Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator. Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings. editor / Huimin Lu ; Michael Blumenstein ; Sung-Bae Cho ; Cheng-Lin Liu ; Yasushi Yagi ; Tohru Kamiya. Springer Science and Business Media Deutschland GmbH, 2023. pp. 15-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Indoor robots are becoming increasingly prevalent across a range of sectors, but the challenge of navigating multi-level structures through elevators remains largely uncharted. For a robot to operate successfully, it{\textquoteright}s pivotal to have an accurate perception of elevator states. This paper presents a robust robotic system, tailored to interact adeptly with elevators by discerning their status, actuating buttons, and boarding seamlessly. Given the inherent issues of class imbalance and limited data, we utilize the YOLOv7 model and adopt specific strategies to counteract the potential decline in object detection performance. Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, Our method effectively confronts the class imbalance and label dependency observed in real-world datasets, offering a promising approach to improve indoor robotic navigation systems.",

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author = "Seungyoun Shin and Lee, {Joon Hyung} and Junhyug Noh and Sungjoon Choi",

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Shin, S, Lee, JH, Noh, J & Choi, S 2023, Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator. in H Lu, M Blumenstein, S-B Cho, C-L Liu, Y Yagi & T Kamiya (eds), Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14406 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 15-28, 7th Asian Conference on Pattern Recognition, ACPR 2023, Kitakyushu, Japan, 5/11/23. https://doi.org/10.1007/978-3-031-47634-1_2

Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator. / Shin, Seungyoun; Lee, Joon Hyung; Noh, Junhyug et al.
Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings. ed. / Huimin Lu; Michael Blumenstein; Sung-Bae Cho; Cheng-Lin Liu; Yasushi Yagi; Tohru Kamiya. Springer Science and Business Media Deutschland GmbH, 2023. p. 15-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14406 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Shin S, Lee JH, Noh J, Choi S. Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator. In Lu H, Blumenstein M, Cho SB, Liu CL, Yagi Y, Kamiya T, editors, Pattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 15-28. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-47634-1_2