Cross-Modal Invariant Representation Learning for Robust Image-to-PointCloud Place Recognition

Image-to-PointCloud place recognition is vital for autonomous systems, yet faces challenges from the inherent modality gap and drastic environmental variations. We propose Cross-Modal Invariant Representation Learning (CMIRL) to learn highly invariant cross-modal global descriptors. CMIRL introduces an Adaptive Cross-Modal Alignment (ACMA) module, which dynamically projects point clouds based on image semantics to generate view-optimized dense depth maps. A Dual-Stream Invariant Feature Encoder, featuring a Transformer-based Cross-Modal Attention Fusion (CMAF) module, then explicitly learns and emphasizes features shared across modalities and insensitive to environmental perturbations. These fused local features are subsequently aggregated into a robust global descriptor using an enhanced multi-scale NetVLAD network. Extensive experiments on the challenging KITTI dataset demonstrate that CMIRL significantly outperforms state-of-the-art methods in terms of top-one recall and overall recall. An ablation study validates the effectiveness of each proposed module, and qualitative analysis confirms enhanced robustness under adverse conditions, including low light, heavy shadows, simulated weather, and significant viewpoint changes. Strong generalization capabilities on an unseen dataset and competitive computational efficiency further highlight CMIRL's potential for reliable long-term autonomous localization.