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

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

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

scholarly journals A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization

2021 ◽  
Vol 13 (14) ◽  
pp. 2720
Author(s):  
Shoubin Chen ◽  
Baoding Zhou ◽  
Changhui Jiang ◽  
Weixing Xue ◽  
Qingquan Li
Keyword(s):  
Point Clouds ◽  
Visual Features ◽  
Visual Slam ◽  
Key Factors ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Active Sensor ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Pose Graph Optimization

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.

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