iBoW-LCD: An Appearance-Based Loop-Closure Detection Approach Using Incremental Bags of Binary Words
Open accessAbstract
In this letter, we introduce iBoW-LCD, a novel appearance-based loop-closure detection method. The presented approach makes use of an incremental bag-of-words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dynamic islands, a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated with Bayesian frameworks. Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A bag-of-words algorithm can fail in completely unknown environments.
“utilizing only the global descriptor instead of a BoW algorithm which can fail in completely unknown environments [48].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [48]
The Introduction (p. 1) says that an offline BoW vocabulary trained on images unlike the current environment can lead to additional false detections. But the paper also presents an incremental BoW method designed to avoid that limitation, so it does not support the claim as a general statement about BoW algorithms in completely unknown environments. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037 · see citing works
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367 · see citing works
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199 · see citing works
BRIEF: Binary Robust Independent Elementary Features
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_56 · Cited by 3,671 · see citing works
Scalable Recognition with a Vocabulary Tree
Nistér, D., & Stewénius, H. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.264 · Cited by 3,599 · see citing works
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.572 · Cited by 3,441 · see citing works
(1999) · Cited by 3,186 · see citing works
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
(2009) · Proceedings of the Fourth International Conference on Computer Vision Theory and Applications · doi:10.5220/0001787803310340 · Cited by 2,640 · see citing works
Bags of Binary Words for Fast Place Recognition in Image Sequences
Gálvez‐López, D., & Tardós, J. D. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2197158 · Cited by 1,890 · see citing works
NetVLAD: CNN architecture for weakly supervised place recognitionPreprint
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2015) · arXiv (Cornell University) · Cited by 1,790 · see citing works
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472 · see citing works
Salakhutdinov, R., & Hinton, G. (2009) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.11.006 · Cited by 1,246 · see citing works
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces
Alcantarilla, P. F., Nuevo, J., & Bartoli, A. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.13 · Cited by 1,210 · see citing works
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
Milford, M. J., & Wyeth, G. F. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224623 · Cited by 981 · see citing works
Optimised KD-trees for fast image descriptor matching
Silpa-Anan, C., & Hartley, R. I. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587638 · Cited by 690 · see citing works
Appearance-only SLAM at large scale with FAB-MAP 2.0
Cummins, M., & Newman, P. M. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910385483 · Cited by 604 · see citing works
On the performance of ConvNet features for place recognition
Sünderhauf, N., Rowlands, S., Dayoub, F., Upcroft, B., & Milford, M. J. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353986 · Cited by 539 · see citing works
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
Angeli, A., Filliat, D., Doncieux, S., & Meyer, J.-A. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004514 · Cited by 506 · see citing works
Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free
Sünderhauf, N., Rowlands, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. J. (2015) · Robotics: Science and Systems · doi:10.15607/rss.2015.xi.022 · Cited by 374 · see citing works
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
Labbé, M., & Michaud, F. (2013) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2242375 · Cited by 372 · see citing works
Fast Matching of Binary Features
Muja, M., & Lowe, D. G. (2012) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2012.60 · Cited by 300 · see citing works
Fast relocalisation and loop closing in keyframe-based SLAM
Mur-Artal, R., & Tardós, J. D. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6906953 · Cited by 262 · see citing works
A visual bag of words method for interactive qualitative localization and mapping
Filliat, D. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.364080 · Cited by 237 · see citing works
Vision-based topological mapping and localization methods: A survey
Garcia-Fidalgo, E., & Ortiz, A. (2015) · Robotics Auton. Syst. · doi:10.1016/j.robot.2014.11.009 · Cited by 185 · see citing works
Self-citationLocal Difference Binary for Ultrafast and Distinctive Feature Description
Yang, X. Y., & Cheng, K.-T. T. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.150 · Cited by 167 · see citing works
Automatic Visual Bag-of-Words for Online Robot Navigation and Mapping
Nicosevici, T., & García, R. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2192013 · Cited by 106 · see citing works
Fusion and binarization of CNN features for robust topological localization across seasons
Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., & Romera, E. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759685 · Cited by 98 · see citing works
Hierarchical Place Recognition for Topological Mapping
Garcia‐Fidalgo, E., & Ortiz, A. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2704598 · Cited by 89 · see citing works
Self-citationIBuILD: Incremental bag of Binary words for appearance based loop closure detection
Khan, S., & Wollherr, D. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139959 · Cited by 84 · see citing works
Fast and effective visual place recognition using binary codes and disparity information
Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., Yebes, J. J., & Bronte, S. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942989 · Cited by 61 · see citing works
Bidirectional loop closure detection on panoramas for visual navigation
Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., Yebes, J. J., & Gamez, S. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856457 · Cited by 47 · see citing works
Zhang, G., Lilly, M. J., & Vela, P. A. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487205 · Cited by 33 · see citing works
Building Location Models for Visual Place Recognition
Stumm, E. S., Christopher Mei, A., & Lacroix, S. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364915570140 · Cited by 29 · see citing works
Efficient decentralized visual place recognition from full-image descriptors
Cieslewski, T., & Scaramuzza, D. (2017) · International Symposium on Multi-Robot and Multi-Agent Systems (MRS) · doi:10.1109/mrs.2017.8250934 · Cited by 28 · see citing works
Fast image mosaicing using incremental bags of binary words
Garcia‐Fidalgo, E., Ortiz, A., Bonnin‐Pascual, F., & Joan P. Company (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487247 · Cited by 25 · see citing works
Self-citationOn the use of binary feature descriptors for loop closure detection
Garcia‐Fidalgo, E., & Ortiz, A. (2014) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2014.7005121 · Cited by 24 · see citing works
Self-citationEfficient decentralized visual place recognition from full-image descriptors
Cieslewski, T., & Scaramuzza, D. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-257407 · Cited by 23 · see citing works
High order visual words for structure-aware and viewpoint-invariant loop closure detection
Bampis, L., Amanatiadis, A., & Gasteratos, A. (2017) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2017.8206289 · Cited by 18 · see citing works
Fast Image Mosaicking Using Incremental Bags of Binary Words
Garcia‐Fidalgo, E., & Ortiz, A. (2018) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-75993-7_8 · Cited by 17 · see citing works
Self-citationMethods for Appearance-based Loop Closure Detection
Garcia‐Fidalgo, E., & Ortiz, A. (2018) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-75993-7 · Cited by 8 · see citing works
Self-citationGarcia‐Fidalgo, E., & Ortiz, A. (2018) · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Keypoint Detection and Description through Deep Learning in Unstructured Environments
Petrakis, G., & Partsinevelos, P. (2023) · Robotics · doi:10.3390/robotics12050137 · Cited by 10 · see citing works
- Authors
- Garcia-Fidalgo, E., & Ortiz, A.
- Affiliations
- Universitat de les Illes Balears (ES)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-10
- Volume
- 3
- Issue
- 4
- Pages
- 3051-3057
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2785901219
- Cited by
- 167
- References
- 44
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 23 · 2023: 25 · 2022: 35 · 2021: 33 · 2020: 13 · 2019: 18 · 2018: 2
- FWCI
- 13.43
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- EU H2020 project ROBINS (779776)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- loop closure detection, binary descriptors, visual SLAM, image retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
