To address the issue of low robustness in visual place recognition due to environmental changes like weather, season and lighting, we propose a solution called parallel omnidimensional dynamic attention (POD-Attention). In order to achieve dynamic and fine-grained exploration of convolutional kernels across all dimensions and enhance the feature extraction network’s ability to capture invariant features like buildings, a complementary attention mechanism is incorporated into the omni-dimensional dynamic convolutional block. This mechanism operates on all dimensions of the convolutional kernels, including input/output channels, convolutional space and kernel quantity, enabling comprehensive attention across the entire kernel space. Furthermore, the parallel fusion of the 1×1 convolution, skip squeeze-and-excitation (SSE) module and omni-dimensional dynamic convolutional block yields notable benefits in terms of both feature extraction speed and the expansion of the receptive field within the visual place recognition network. By combining these components in parallel, the network gains the ability to capture more comprehensive information, resulting in enhanced accuracy for visual place recognition tasks. Experiments conducted on public datasets show that the visual place recognition method based on VGG16 and Patch-NetVLAD feature aggregation improved by the POD attention mechanism, achieves 9.7% increase in Recall@1 on the Nordland dataset and 1.8% increase on the Mapillary Street-Level Sequences dataset. These results demonstrate that the proposed POD attention mechanism effectively enhances the robustness of visual place recognition in different environmental conditions, laying a foundation for more accurate visual localization and map construction in visual SLAM.