详细信息
- 来源站点
- ArXiv CS.AI
- 作者
- Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-06-30
摘要
arXiv:2606.28607v1 Announce Type: cross Abstract: 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.