MFF-PR: Point Cloud and Image Multi-modal Feature Fusion for Place Recognition

Place recognition technology can eliminate cumulative errors and thus plays a vital role in autonomous driving. In this paper, the composite feature of point cloud and image data is obtained by multi-modal feature fusion, thereby improving positioning accuracy. Semantic features, instance features, topological features, and image texture features are integrated to obtain comprehensive features, presenting strong robustness and complex scene expression abilities. Topological features consist of intra-class features and inter-instance features, which allow users to obtain more comprehensive scene structure information. The place recognition methods of data-level fusion and feature-level fusion based on point cloud and image are compared. This paper verifies the proposed method on SemanticKitti and nuScenes datasets. The results show that it outperforms state-of-the-art place recognition methods.