Open AccessHigh Performance Online Loop Closure Detection for Topological Mapping
Author(s) -
Zhaowei Shi,
Jingyu Luo,
Yunfeng Wang,
Jinfeng Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012117
Subject(s) - loop (graph theory) , closing (real estate) , similarity (geometry) , computer science , closure (psychology) , matching (statistics) , for loop , artificial intelligence , simultaneous localization and mapping , computer vision , pattern recognition (psychology) , robot , precision and recall , topology (electrical circuits) , mathematics , image (mathematics) , mobile robot , market economy , statistics , combinatorics , political science , economics , law
In simultaneous localization and mapping (SLAM) system, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the precise self-localisation of the robot; however, the performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. In the traditional likelihood matching methods, the number of matching words and the difference between them are not considered. This paper proposes a method based on mixed similarity to calculate the similarity score, thereby improving the performance of closed-loop detection. Experiments are performed on datasets from dynamic environments and visual repetitive environments, and then this method can produce a higher recall rate with 100% accuracy compared to the latest methods.