Work

Fast and Accurate Outlier-Aware Lidar Super-Resolution for Slam Applications

Year: 2025

Type: conference-paper

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.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip55913.2025.11084730

Authors: Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos

Institution: Industrial Systems Institute

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, HORIZON EUROPE Framework Programme

Awards: Autonomous self-adaptive services for TRansformational personalized inclUsivenesS and resilience in mobiliTy, Enhancing Robustness and Security of Edge AI Systems for Safety-Critical Applications