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Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications

2026-06-26 20:55:16
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
arXiv_AI
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2606.28607

PDF

https://arxiv.org/pdf/2606.28607.pdf

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