Abstract: Most existing point cloud-based place recognition methods emphasize feature extraction from individual points or local regions, while largely neglecting the relational information embedded within local neighborhoods. As a result, they often fail to capture discriminative relational patterns, leading to reduced recognition accuracy in scenes containing geometrically similar structures. In this paper, we propose a novel relation-aware network (RA-Net) for place recognition. RA-Net jointly exploits local relational cues and global contextual information to learn discriminative scene representations for large-scale point cloud-based place recognition. First, a spatial relation feature extraction (SRFE) module is proposed to exploit relational information embedded within local neighborhoods. By learning relation-aware weights and adaptively aggregating neighborhood information, the proposed module captures discriminative relational patterns by jointly considering feature discrepancies and spatial offsets. Furthermore, a global feature extraction (GFE) module is introduced to aggregate global feature statistics and integrate them with pointwise representations, enabling local features to be enhanced with global contextual cues. Experimental results on four benchmark datasets demonstrate that RA-Net can generate more discriminative global descriptors and achieve promising performance. It exhibits strong generalization capabilities for unseen scenes.

基于关系感知的大规模点云地点识别网络

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