Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-28384| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments |
| Authors: | Fathi, Kiavash Darvishy, Alireza van de Venn, Hans Wernher |
| et. al: | No |
| DOI: | 10.1109/COINS57856.2023.10189300 10.21256/zhaw-28384 |
| Proceedings: | 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS) |
| Page(s): | 7 |
| Conference details: | IEEE International Conference on omni-layer intelligent Systems (COINS), Berlin, Germany and online, 23-35 July 2023 |
| Issue Date: | 2023 |
| Publisher / Ed. Institution: | IEEE |
| ISBN: | 979-8-3503-4647-3 |
| Language: | English |
| Subjects: | Visual simultaneous localization and mapping; Localization; Visual odometry; Autonomous vehicles; Loop closure detection; Real-time system; Computational modeling |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | Loop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28384 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) Institute of Mechatronic Systems (IMS) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Fathi-etal_Similarity-and-location-based-real-time-loop-closure_IEEE_COINS_AAM.pdf | 850.22 kB | Accepted Version | View/Open |
Show full item record
Fathi, K., Darvishy, A., & van de Venn, H. W. (2023). Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments [Conference paper]. 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 7. https://doi.org/10.1109/COINS57856.2023.10189300
Fathi, K., Darvishy, A. and van de Venn, H.W. (2023) ‘Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments’, in 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, p. 7. Available at: https://doi.org/10.1109/COINS57856.2023.10189300.
K. Fathi, A. Darvishy, and H. W. van de Venn, “Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments,” in 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2023, p. 7. doi: 10.1109/COINS57856.2023.10189300.
FATHI, Kiavash, Alireza DARVISHY und Hans Wernher VAN DE VENN, 2023. Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments. In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS). Conference paper. IEEE. 2023. S. 7. ISBN 979-8-3503-4647-3
Fathi, Kiavash, Alireza Darvishy, and Hans Wernher van de Venn. 2023. “Similarity and Location-Based Real-Time Loop Closure : SNAPS for SLAM in Unexplored-Environments.” Conference paper. In 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 7. IEEE. https://doi.org/10.1109/COINS57856.2023.10189300.
Fathi, Kiavash, et al. “Similarity and Location-Based Real-Time Loop Closure : SNAPS for SLAM in Unexplored-Environments.” 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), IEEE, 2023, p. 7, https://doi.org/10.1109/COINS57856.2023.10189300.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.