SeqSLAM with Bag of Visual Words for Appearance Based Loop Closure Detection

2018 ◽  
pp. 580-587
Author(s):  
Konstantinos A. Tsintotas ◽  
Loukas Bampis ◽  
Stelios Rallis ◽  
Antonios Gasteratos
Keyword(s):  
Bag Of Visual Words ◽  
Loop Closure ◽  
Visual Words ◽  
Loop Closure Detection

scholarly journals Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words

2008 ◽  
Vol 24 (5) ◽  
pp. 1027-1037 ◽  
Author(s):  
A. Angeli ◽  
D. Filliat ◽  
S. Doncieux ◽  
J.-A. Meyer
Keyword(s):  
Loop Closure ◽  
Visual Words ◽  
Loop Closure Detection ◽  
Incremental Method ◽  
Bags Of Visual Words

scholarly journals Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Gangchen Hua ◽  
Xu Tan
Keyword(s):  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Geometric Constraints ◽  
Long Term Memory ◽  
Affine Invariant ◽  
Loop Closure ◽  
Visual Words ◽  
Loop Closure Detection ◽  
Localization And Mapping

In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM) using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera’s intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM) to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.

Modest-vocabulary loop-closure detection with incremental bag of tracked words

2021 ◽  
pp. 103782
Author(s):  
Konstantinos A. Tsintotas ◽  
Loukas Bampis ◽  
Antonios Gasteratos
Keyword(s):  
Loop Closure ◽  
Loop Closure Detection

scholarly journals Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

2020 ◽  
Author(s):  
Cedric Le Gentil ◽  
Mallikarjuna Vayugundla ◽  
Riccardo Giubilato ◽  
Wolfgang Sturzl ◽  
Teresa Vidal-Calleja ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Planetary Environments

scholarly journals FastLCD: A fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping

2021 ◽  
Vol 102 ◽  
pp. 102430
Author(s):  
Haodong Xiang ◽  
Wenzhong Shi ◽  
Wenzheng Fan ◽  
Pengxin Chen ◽  
Sheng Bao ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Detection Approach
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