LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes
This addresses computational and robustness issues in visual SLAM for robotics and autonomous systems, though it appears incremental as it builds on existing appearance-based methods.
The paper tackled loop closure detection in visual SLAM by introducing LSGDDN-LCD, which uses local superpixel grid descriptors and incremental dynamic nodes to improve efficiency and handle dynamic scenes, achieving better precision-recall and outperforming state-of-the-art methods on multiple datasets.
Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint variance, and dynamic objects in scenes. This paper introduced an online appearance based LCD using local superpixel grids descriptor and dynamic node, i.e, LSGDDN-LCD, to find similarities between scenes via hand-crafted features extracted from LSGD. Unlike traditional Bag-of-Words (BoW) based LCD, which requires pre-training, we proposed an adaptive mechanism to group similar images called $\textbf{\textit{dynamic}}$ $\textbf{\textit{node}}$, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision-recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on multiple typical datasets, indicating its great potential as a generic LCD framework.