Patterns of approximated localised moments for visual loop closure detection

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 realtime 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.