# Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications Source: [Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications]() Collection index: [llms.txt]() Data updated: 2026-07-01T05:00:04.992Z Authors: Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos Published: 2026-06-26T20:55:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [SLAM Pose Estimation on Ouster LiDAR OS-1-64 (4,000-scan sequence)](): SLAM Pose Estimation; Ouster LiDAR OS-1-64 (4,000-scan sequence); RMSE (m): 2.35. - [SLAM Pose Estimation on Ouster LiDAR OS-1-64 (8,000-scan sequence)](): SLAM Pose Estimation; Ouster LiDAR OS-1-64 (8,000-scan sequence); RMSE (m): 22.64. ## Original sources and implementation [Original paper]() [PDF]()