RangePlace: A Hierarchical Range Image Transformer for LiDAR-Based Place Recognition

LiDAR-based place recognition contributes significantly to identifying loop closure candidates for long-term simultaneous localization and mapping (SLAM) systems and obtaining global position for intelligent vehicles. Although the existing Transformer-based methods have achieved impressive results, they lack the capability of harnessing Transformers to establish long-range dependencies and suffer from high computational costs. To address these limitations, this study proposes a hierarchical range image transformer network named RangePlace to generate discriminative global descriptors for place recognition. By leveraging shifted window-based attention and depth-wise convolution, the proposed network can effectively and efficiently extract both global and local features. This not only enhances the network's capability to learn long-range information but also reduces the computational costs. In addition, a Pyramid Feature Mix module is incorporated to aggregate multi-scale feature maps to unified global descriptor, thus significantly enhancing place recognition performance. The proposed method is verified by experiments on the KITTI Odometry dataset, NCLT dataset, Ford Campus dataset and NuScenes dataset. The results demonstrate that the proposed method can achieve state-of-the-art performance in terms of recall rates. Moreover, it has good robustness to viewpoint yaw-rotation and strong generalization ability in various environments. Furthermore, it can be applied to real-time localization systems. The open-sourced codes used in this study are publicly available at https://github.com/JiLiBIT/RangePlace.