Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
Dominating Set Database Selection for Visual Place Recognition. / Корнилова, Анастасия Валерьевна; Москаленко, Иван Николаевич; Пушкин, Тимофей Дмитриевич; Tojiboev, Fakhriddin; Tariverdizadeh, Rahim; Ferrer, Gonzalo.
2023 21st International Conference on Advanced Robotics (ICAR): 5-8 December 2023. Abu Dhabi, UAE. IEEE Xplore : Institute of Electrical and Electronics Engineers Inc., 2023. стр. 473-479 (International Conference on Advanced Robotics (ICAR)).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
}
TY - GEN
T1 - Dominating Set Database Selection for Visual Place Recognition
AU - Корнилова, Анастасия Валерьевна
AU - Москаленко, Иван Николаевич
AU - Пушкин, Тимофей Дмитриевич
AU - Tojiboev, Fakhriddin
AU - Tariverdizadeh, Rahim
AU - Ferrer, Gonzalo
N1 - Conference code: 21
PY - 2023/12/7
Y1 - 2023/12/7
N2 - This paper introduces a novel approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed method formulates the problem as a minimization challenge by utilizing a dominating set algorithm applied to a graph constructed from spatial information, referred to as the “DominatingSet” algorithm. Experimental results on various datasets, including 7-scenes, BundleFusion, RISEdb, and a specifically recorded sequences in a highly repetitive office setting, demonstrate that our technique significantly reduces database size while maintaining comparable VPR performance to state-of-the-art approaches in challenging environments. Additionally, our solution enables weakly-supervised labeling for all images from the sequences, facilitating the automatic fine-tuning of VPR algorithm to target environment. Additionally, this paper presents a fully automated pipeline for creating VPR databases from RGBD scanning sequences and introduces a set of metrics for evaluating the performance of VPR databases. The code and released data are available on our web-page — https://prime-slam.github.io/place-recognition-db/.
AB - This paper introduces a novel approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed method formulates the problem as a minimization challenge by utilizing a dominating set algorithm applied to a graph constructed from spatial information, referred to as the “DominatingSet” algorithm. Experimental results on various datasets, including 7-scenes, BundleFusion, RISEdb, and a specifically recorded sequences in a highly repetitive office setting, demonstrate that our technique significantly reduces database size while maintaining comparable VPR performance to state-of-the-art approaches in challenging environments. Additionally, our solution enables weakly-supervised labeling for all images from the sequences, facilitating the automatic fine-tuning of VPR algorithm to target environment. Additionally, this paper presents a fully automated pipeline for creating VPR databases from RGBD scanning sequences and introduces a set of metrics for evaluating the performance of VPR databases. The code and released data are available on our web-page — https://prime-slam.github.io/place-recognition-db/.
KW - Location awareness
KW - Visualization
KW - Codes
KW - Pipelines
KW - Indoor environment
KW - Visual databases
KW - Robots
U2 - 10.1109/ICAR58858.2023.10406721
DO - 10.1109/ICAR58858.2023.10406721
M3 - Conference contribution
SN - 979-8-3503-4230-7
T3 - International Conference on Advanced Robotics (ICAR)
SP - 473
EP - 479
BT - 2023 21st International Conference on Advanced Robotics (ICAR)
PB - Institute of Electrical and Electronics Engineers Inc.
CY - IEEE Xplore
T2 - 21st International Conference on Advanced Robotics (ICAR 2023)
Y2 - 5 December 2023 through 8 December 2023
ER -
ID: 116521508