Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM

2018 ◽  
Vol 42 (7) ◽  
pp. 1323-1335 ◽  
Author(s):  
Fei Han ◽  
Hua Wang ◽  
Guoquan Huang ◽  
Hao Zhang
Keyword(s):  
Optimization Methods ◽  
Sparse Optimization ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection

Edge-Based Loop Closure Detection in Visual SLAM

2018 ◽  
Author(s):  
Dario F. Mendieta ◽  
Francisco Raverta Capua ◽  
Juan Jose Tarrio ◽  
Marcelo L. Moreyra
Keyword(s):  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Edge Based

scholarly journals Long-Term Loop Closure Detection through Visual-Spatial Information Preserving Multi-Order Graph Matching

2020 ◽  
Vol 34 (06) ◽  
pp. 10369-10376
Author(s):  
Peng Gao ◽  
Hao Zhang
Keyword(s):  
Spatial Information ◽  
Graph Matching ◽  
Fundamental Problem ◽  
Graph Representation ◽  
Spatial Relationships ◽  
Matching Problem ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Visual Spatial

Loop closure detection is a fundamental problem for simultaneous localization and mapping (SLAM) in robotics. Most of the previous methods only consider one type of information, based on either visual appearances or spatial relationships of landmarks. In this paper, we introduce a novel visual-spatial information preserving multi-order graph matching approach for long-term loop closure detection. Our approach constructs a graph representation of a place from an input image to integrate visual-spatial information, including visual appearances of the landmarks and the background environment, as well as the second and third-order spatial relationships between two and three landmarks, respectively. Furthermore, we introduce a new formulation that formulates loop closure detection as a multi-order graph matching problem to compute a similarity score directly from the graph representations of the query and template images, instead of performing conventional vector-based image matching. We evaluate the proposed multi-order graph matching approach based on two public long-term loop closure detection benchmark datasets, including the St. Lucia and CMU-VL datasets. Experimental results have shown that our approach is effective for long-term loop closure detection and it outperforms the previous state-of-the-art methods.

Loop Closure Detection based on Image Covariance Matrix Matching for Visual SLAM

2021 ◽  
Author(s):  
Tao Ying ◽  
Huaicheng Yan ◽  
Zhichen Li ◽  
Kaibo Shi ◽  
Xiangsai Feng
Keyword(s):  
Covariance Matrix ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection

Loop Closure Detection of Visual SLAM Based on Improved LBD and Data-Dependent Measure

2021 ◽  
Vol 58 (6) ◽  
pp. 0615001
Author(s):  
史佳豪 Shi Jiahao ◽  
孟庆浩 Meng Qinghao ◽  
戴旭阳 Dai Xuyang
Keyword(s):  
Dependent Measure ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection
Sign in / Sign up

Export Citation Format

Share Document