Robust Visual Loop Closure Detection with Repetitive Features

Seongwon Lee, Hyung Gi Jo, Hae Min Cho, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Loop closure detection problem is an essential issue in simultaneous localization and mapping (SLAM) problem. In particular, visual loop closure detection, which using a visual sensor, should be robust to environmental conditions like confusion caused by repeated structures. In this paper, we propose a robust visual loop closure detection algorithm through restrained repetitive features observed in repeating structures. The proposed algorithm aims to extract bag of visual words (BoVW) for each image frame with RootSIFT extraction, improve it by restrain dominantly repetitive features, calculates histogram similarity score with histogram comparing method and finally decides loop closure pair(s). Experimental results show that the proposed algorithm robustly performs loop closure detection.

Original languageEnglish
Title of host publication2018 15th International Conference on Ubiquitous Robots, UR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages891-895
Number of pages5
ISBN (Print)9781538663349
DOIs
Publication statusPublished - 2018 Aug 20
Event15th International Conference on Ubiquitous Robots, UR 2018 - Honolulu, United States
Duration: 2018 Jun 272018 Jun 30

Publication series

Name2018 15th International Conference on Ubiquitous Robots, UR 2018

Other

Other15th International Conference on Ubiquitous Robots, UR 2018
CountryUnited States
CityHonolulu
Period18/6/2718/6/30

Fingerprint

Closure
Histogram
Simultaneous Localization and Mapping
Sensors
Vision
Calculate
Sensor
Experimental Results

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization
  • Mechanical Engineering

Cite this

Lee, S., Jo, H. G., Cho, H. M., & Kim, E. (2018). Robust Visual Loop Closure Detection with Repetitive Features. In 2018 15th International Conference on Ubiquitous Robots, UR 2018 (pp. 891-895). [8441872] (2018 15th International Conference on Ubiquitous Robots, UR 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URAI.2018.8441872
Lee, Seongwon ; Jo, Hyung Gi ; Cho, Hae Min ; Kim, Euntai. / Robust Visual Loop Closure Detection with Repetitive Features. 2018 15th International Conference on Ubiquitous Robots, UR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 891-895 (2018 15th International Conference on Ubiquitous Robots, UR 2018).
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abstract = "Loop closure detection problem is an essential issue in simultaneous localization and mapping (SLAM) problem. In particular, visual loop closure detection, which using a visual sensor, should be robust to environmental conditions like confusion caused by repeated structures. In this paper, we propose a robust visual loop closure detection algorithm through restrained repetitive features observed in repeating structures. The proposed algorithm aims to extract bag of visual words (BoVW) for each image frame with RootSIFT extraction, improve it by restrain dominantly repetitive features, calculates histogram similarity score with histogram comparing method and finally decides loop closure pair(s). Experimental results show that the proposed algorithm robustly performs loop closure detection.",
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Lee, S, Jo, HG, Cho, HM & Kim, E 2018, Robust Visual Loop Closure Detection with Repetitive Features. in 2018 15th International Conference on Ubiquitous Robots, UR 2018., 8441872, 2018 15th International Conference on Ubiquitous Robots, UR 2018, Institute of Electrical and Electronics Engineers Inc., pp. 891-895, 15th International Conference on Ubiquitous Robots, UR 2018, Honolulu, United States, 18/6/27. https://doi.org/10.1109/URAI.2018.8441872

Robust Visual Loop Closure Detection with Repetitive Features. / Lee, Seongwon; Jo, Hyung Gi; Cho, Hae Min; Kim, Euntai.

2018 15th International Conference on Ubiquitous Robots, UR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 891-895 8441872 (2018 15th International Conference on Ubiquitous Robots, UR 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Loop closure detection problem is an essential issue in simultaneous localization and mapping (SLAM) problem. In particular, visual loop closure detection, which using a visual sensor, should be robust to environmental conditions like confusion caused by repeated structures. In this paper, we propose a robust visual loop closure detection algorithm through restrained repetitive features observed in repeating structures. The proposed algorithm aims to extract bag of visual words (BoVW) for each image frame with RootSIFT extraction, improve it by restrain dominantly repetitive features, calculates histogram similarity score with histogram comparing method and finally decides loop closure pair(s). Experimental results show that the proposed algorithm robustly performs loop closure detection.

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Lee S, Jo HG, Cho HM, Kim E. Robust Visual Loop Closure Detection with Repetitive Features. In 2018 15th International Conference on Ubiquitous Robots, UR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 891-895. 8441872. (2018 15th International Conference on Ubiquitous Robots, UR 2018). https://doi.org/10.1109/URAI.2018.8441872