Hierarchical loop closure detection with weighted local patch features and global descriptors

Maintaining high-precision localization and ensuring map consistency are crucial objectives for mobile robots. However, loop closure detection remains a challenging aspect of their operation because of viewpoint and appearance changes. To address this issue, this paper proposes WP-VLAD, a novel hierarchical loop closure detection method that tightly couples global features and weighted local patch-level features (WPs). WP-VLAD employs MobileNetV3 as the backbone network for feature extraction, and integrates a trainable vector of local aggregated descriptors (VLAD) for compact global and local feature representation. A hierarchical navigable small world method is used to retrieve loop candidate frames based on the global features, whereas a multiscale feature fusion weighted map prediction module assigns weights to the local patches during mutual nearest neighbour matching. The proposed weight allocation strategy emphasizes salient regions, reducing interference from dynamic objects. The experimental results on benchmark datasets demonstrate that WP-VLAD significantly improves matching performance while maintaining efficient computation, exhibiting strong generalizability and robustness across various complex environments.