LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916784092741632 |
|---|---|
| author | Nakshbandi, Mohammad-Maher Sharawy, Ziad Cojocaru, Dorian Grigorescu, Sorin |
| author_facet | Nakshbandi, Mohammad-Maher Sharawy, Ziad Cojocaru, Dorian Grigorescu, Sorin |
| contents | In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising over 1000 images captured across diverse environments, including parks, indoor scenes, parking spaces, as well as centered around individual objects. Each scene is represented by a sequence of five consecutive images. The dataset was collected using a high resolution camera, providing suitable imagery for benchmarking the accuracy of loop closure algorithms, typically used in simultaneous localization and mapping. As ground truth information, we provide computed rotations and translations between each consecutive images. Additional to its benchmarking goal, the dataset can be used to train and fine-tune loop closure methods based on deep neural networks. LoopDB is publicly available at https://github.com/RovisLab/LoopDB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping Nakshbandi, Mohammad-Maher Sharawy, Ziad Cojocaru, Dorian Grigorescu, Sorin Computer Vision and Pattern Recognition Robotics In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising over 1000 images captured across diverse environments, including parks, indoor scenes, parking spaces, as well as centered around individual objects. Each scene is represented by a sequence of five consecutive images. The dataset was collected using a high resolution camera, providing suitable imagery for benchmarking the accuracy of loop closure algorithms, typically used in simultaneous localization and mapping. As ground truth information, we provide computed rotations and translations between each consecutive images. Additional to its benchmarking goal, the dataset can be used to train and fine-tune loop closure methods based on deep neural networks. LoopDB is publicly available at https://github.com/RovisLab/LoopDB. |
| title | LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2506.06771 |