用深度展开方法提升低分辨率激光雷达点云，实时去噪增强定位精度。
Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications
- 基于模型优化的深度展开框架，逐层迭代重建点云。
- 在SLAM中实现更准的位姿估计，速度优于现有方法。
- 适合对实时性与精度有要求的自动驾驶场景。
本文针对SLAM应用中低分辨率激光雷达的增强问题，提出一种基于深度展开的超分辨率（SR）模型。通过引入异常值去除模块，在保持结构完整性的同时实现实时性能。该方法利用基于模型的优化策略，高效重建高分辨率点云，同时最小化计算开销。所提SR模型在激光雷达SLAM框架中进行评估，相比当前最优方法，在位姿估计准确性和效率方面均有显著提升。
原文摘要 · Abstract (English)
This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution (SR) model. We integrate an outlier removal module to ensure structural integrity while maintaining real-time performance. By leveraging a model-based optimization approach, our method efficiently reconstructs high-resolution point clouds while minimizing computational overhead. The proposed SR model is evaluated within a LiDAR SLAM framework, demonstrating significant improvements in pose estimation accuracy and efficiency compared to state-of-the-art SR methods.
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