Localization is a crucial component of robotic systems. LiDAR odometry provides robust and real-time localization but suffers from significant cumulative errors in the absence of loop closures. On the other hand, local localization based on prior maps is not affected by cumulative errors but can fail when faced with dynamic changes in the map during long-term localization scenarios. To address these issues, we propose a tightly coupled LiDAR-inertial odometry(LIO) and prior information localization system that simultaneously solves the problems of cumulative error and localization failure in dynamic scenes. Our system employs local localization to quickly track the prior map, thereby reducing odometry’s cumulative error and ensuring robust performance even in areas with significant scene changes. Additionally, we introduce a global localization strategy to rapidly correct cumulative errors when odometry tracking of the prior map fails. We evaluated our approach on the MulRan dataset, and the results demonstrate that our method effectively resolves localization failures and cumulative error issues during long-term localization. Furthermore, we validated the feasibility of our method in a real-world logistics factory environment. The results indicate that our method performs well in complex and dynamic logistics factory scenarios.

Recently, the application of Transformer in computer vision has shown us the potential of this new paradigm. However, standard multi-head Attention (MSA) faces an explosion of computational cost as the input changes from a sequence of text to an image, and MSA is computationally redundant for images. In this paper, we propose a new backbone network combining window-based attention and convolutional neural networks named ConMW Transformer, introducing convolution into the Transformer to help it converge quickly and improve accuracy. ConMW Transformer use a hierarchical architecture, an inductive bias is incorporated during tokenization and feature projection. We reduce the computational cost by performing the attention operation within windows after partition the feature map, while allowing connections between multiple heads for a more appropriate joint representation. We also use large kernel convolution after the window-based attention to merge features between windows, which help maintaining the superiority of attention in global context modelling. With only ImageNet-1K pre-training using 224 × 224 resolution, our base model can achieve 83.7% top-1 accuracy on ImageNet-1K and 49.9 mIoU for semantic segmentation on ADE20K.

3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.