Lu Xiong

Boyuan Zheng, Shouyi Lu, Renbo Huang et al.

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose the Latent Voxel Diffusion Model (LVDM), which performs the diffusion process in the latent space. Additionally, a novel Latent Point Cloud Reconstruction (LPCR) module is utilized to reconstruct point clouds from high-dimensional latent voxel features. As a result, R2LDM effectively generates LiDAR-like point clouds from paired raw radar data. We evaluate our approach on two different datasets, and the experimental results demonstrate that our model achieves 6- to 10-fold densification of radar point clouds, outperforming state-of-the-art baselines in 4D radar point cloud super-resolution. Furthermore, the enhanced radar point clouds generated by our method significantly improve downstream tasks, achieving up to 31.7% improvement in point cloud registration recall rate and 24.9% improvement in object detection accuracy.

Jiaqi Zhu, Shouyi Lu, Ziyao Li et al.

Global Navigation Satellite Systems (GNSS) are vital for reliable urban positioning. However, multipath and non-line-of-sight reception often introduce large measurement errors that degrade accuracy. Learning-based methods for predicting and compensating pseudorange errors have gained traction, but their performance is limited by complex error distributions. To address this challenge, we propose Diff-GNSS, a coarse-to-fine GNSS measurement (pseudorange) error estimation framework that leverages a conditional diffusion model to capture such complex distributions. Firstly, a Mamba-based module performs coarse estimation to provide an initial prediction with appropriate scale and trend. Then, a conditional denoising diffusion layer refines the estimate, enabling fine-grained modeling of pseudorange errors. To suppress uncontrolled generative diversity and achieve controllable synthesis, three key features related to GNSS measurement quality are used as conditions to precisely guide the reverse denoising process. We further incorporate per-satellite uncertainty modeling within the diffusion stage to assess the reliability of the predicted errors. We have collected and publicly released a real-world dataset covering various scenes. Experiments on public and self-collected datasets show that DiffGNSS consistently outperforms state-of-the-art baselines across multiple metrics. To the best of our knowledge, this is the first application of diffusion models to pseudorange error estimation. The proposed diffusion-based refinement module is plug-and-play and can be readily integrated into existing networks to markedly improve estimation accuracy.