Robust Visual Place Recognition Under Variational Views

Visual place recognition (VPR) has played an essential role in simultaneous localization and mapping-based mobile robotics and autonomous driving in the past decade, which can identify previously visited places by matching the current observed view against a view database for global localization and loop closure. However, existing VPR methods always suffer from false place recognition due to the following issues: insensitive spatial–temporal embedding extraction, lack of multiview descriptor for matching, and misconsideration of false views for model optimization. To address these issues, a novel robust framework called VPR under variational views (VPR-VV) is proposed. VPR-VV is integrated with: a sequence encoder to extract robust spatial–temporal features from a view sequence, then a hierarchical view retrieval module is employed for multiview feature descriptor aggregation, and a novel enhanced ranking feedback average precision loss with the normalized discounted cumulative gain metric is designed for model optimization. As a result, VPR-VV can significantly enhance the accuracy and robustness of VPR for robot localization under variational views. Experiments with ablation studies are conducted on various challenging indoor and outdoor datasets, and our framework’s superiority is demonstrated: VPR-VV outperforms state-of-the-art (SOTA) methods by up to 9.4% in recall@1, and real-time inference is achieved without additional memory or computational overhead.