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

2026-06-26

AI Summary

This work addresses the challenge of insufficient point cloud precision from low-resolution LiDAR in SLAM systems. To overcome this limitation, the authors propose a point cloud super-resolution method that integrates depth upsampling with model-driven optimization, incorporating an outlier rejection module to simultaneously preserve structural integrity and ensure real-time performance. The approach is efficiently embedded within a LiDAR SLAM framework, significantly reducing computational overhead while enhancing pose estimation accuracy and overall system efficiency. Experimental results demonstrate that the proposed method outperforms existing super-resolution techniques in both accuracy and computational cost, making it particularly suitable for resource-constrained robotic applications.

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.

Problem

• LiDAR Super-Resolution
• SLAM
• Outlier Removal
• Point Cloud Reconstruction
• Real-time Performance

Innovation

• Deep Unrolling
• Outlier-Aware Super-Resolution
• LiDAR SLAM
• Model-Based Optimization
• Real-Time Point Cloud Reconstruction