Breaking the Frame: Visual Place Recognition by Overlap Prediction
This addresses a key challenge in robotics and autonomous systems for more reliable localization in complex environments, though it is an incremental improvement over existing methods.
The paper tackles the problem of visual place recognition under occlusions and partial overlaps by proposing VOP, an approach based on overlap prediction, which improves accuracy in relative pose estimation and localization on large-scale indoor and outdoor benchmarks compared to state-of-the-art methods.
Visual place recognition methods struggle with occlusions and partial visual overlaps. We propose a novel visual place recognition approach based on overlap prediction, called VOP, shifting from traditional reliance on global image similarities and local features to image overlap prediction. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone and establishing patch-to-patch correspondences without requiring expensive feature detection and matching. Our approach uses a voting mechanism to assess overlap scores for potential database images. It provides a nuanced image retrieval metric in challenging scenarios. Experimental results show that VOP leads to more accurate relative pose estimation and localization results on the retrieved image pairs than state-of-the-art baselines on a number of large-scale, real-world indoor and outdoor benchmarks. The code is available at https://github.com/weitong8591/vop.git.