Large-scale image mosaicking using multimodal hyperedge constraints from multiple registration methods within the Generalized Graph SLAM framework

摘要

Underwater image mosaicking is an important tool for visual surveys, object detection, and as a means to control the underwater robot if done online. Such application areas can benefit significantly from a recent focus on robust methods for graph-based Simultaneous Localization and Mapping (SLAM). This paper focuses on two contributions: An approach to combine registration results from multiple methods in multimodal constraints and, up to the authors' knowledge, the first method to generate hyperedge constraints from state-of-the-art place recognition techniques. Both contributions are implemented within the Generalized Graph SLAM framework. Experimental results show that the methods generate informative constraints and that the authors' Prefilter method outperforms related methods on a large underwater image dataset processed with these methods.