DyLPR: Dynamic Occlusion Inpainting-Enhanced LiDAR Place Recognition for Dynamic Traffic Environments

High-frequency dynamic targets introduce substantial appearance variations in LiDAR scans of the same location over time, posing a major challenge for place recognition. To tackle this, we propose DyLPR, a cascaded PR framework that integrates a LiDAR depth inpainting network and a place recognition network (PTN-Net), leveraging the complementary strengths of convolutional neural networks (CNNs) and transformer architectures. Specifically, a supervised encoder–decoder combining CNNs and transfor