Robust Visual Localization via Semantic-Guided Multi-Scale Transformer
This work addresses robust visual localization for applications in real-world dynamic environments, representing an incremental improvement through the integration of multi-scale processing and semantic guidance.
The paper tackled the problem of visual localization in dynamic environments by proposing a framework that combines multi-scale feature learning with semantic scene understanding, achieving improved performance over existing pose regression methods on the TartanAir dataset in scenarios with dynamic objects, illumination changes, and occlusions.
Visual localization remains challenging in dynamic environments where fluctuating lighting, adverse weather, and moving objects disrupt appearance cues. Despite advances in feature representation, current absolute pose regression methods struggle to maintain consistency under varying conditions. To address this challenge, we propose a framework that synergistically combines multi-scale feature learning with semantic scene understanding. Our approach employs a hierarchical Transformer with cross-scale attention to fuse geometric details and contextual cues, preserving spatial precision while adapting to environmental changes. We improve the performance of this architecture with semantic supervision via neural scene representation during training, guiding the network to learn view-invariant features that encode persistent structural information while suppressing complex environmental interference. Experiments on TartanAir demonstrate that our approach outperforms existing pose regression methods in challenging scenarios with dynamic objects, illumination changes, and occlusions. Our findings show that integrating multi-scale processing with semantic guidance offers a promising strategy for robust visual localization in real-world dynamic environments.