Open AccessPatterns of approximated localised moments for visual loop closure detection
Author(s) -
Erhan Can,
Sariyanidi Evangelos,
Sencan Onur,
Temeltas Hakan
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0237
Subject(s) - artificial intelligence , closing (real estate) , computer science , computer vision , aliasing , context (archaeology) , closure (psychology) , mobile robot , loop (graph theory) , robot , for loop , pattern recognition (psychology) , mathematics , geography , archaeology , combinatorics , undersampling , political science , economics , law , market economy
In the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the accurate self‐localisation of a robot; however, it is also challenging due to perceptual aliasing, which occurs when the robot traverses in environments with visually similar places (e.g. forests, parks, office corridors). In this study, the authors apply the local Zernike moments (ZMs) for loop closure detection. When computed locally, ZMs provide a high discrimination ability, which enables the distinguishing of similar‐looking places. Particularly, they show that increasing the density over which the local ZMs are computed improves loop closing accuracy significantly. Furthermore, they present an approximation of ZMs that allows the usage of integral images, which enable real‐time operation. Experiments on real datasets with strong perceptual aliasing show that the proposed ZM‐based descriptor outperforms state‐of‐the‐art methods in terms of loop closure accuracy. They also release the source‐code of the implementation for research purposes.