LSW-Net: A Spatio-temporal Self-Supervised Framework for 2D LiDAR-Based Environment Perception

Abstract

In the deep learning era, 2D LiDAR perception is often overlooked as research prioritizes 3D point clouds. Yet, 2D LiDAR remains essential for low-cost robotic systems due to its affordability. Despite its simplicity, it faces major challenges-not only in perception but also in learning itself, as data sparsity and limited features hinder effective framework development. Additionally, fundamental structural differences prevent direct adaptation of 3D perception networks. To address these aforementioned concerns, we proposes LSW-Net (Laser Scan Weight-Net), a self-supervised framework for 2D LiDAR perception, enabling end-to-end learning from raw point clouds to adaptive weight extraction. This framework provides a scalable and lightweight solution for 2D LiDAR environmental perception, and its self-supervised nature reduces annotation costs.It includes: (i) a general 2D Laser Encoder (LS-Encoder) that integrates local convolutional perception with global attention perception to extract multi-scale spatio-temporal features; and (ii) an interpretable weight extraction module (Weight Extractor) that dynamically quantifies the contribution of each point in environmental perception tasks through contrastive learning and physical consistency constraints. Evaluated on diverse scenes for point cloud registration and SLAM tasks, LSW-Net outperforms traditional methods in feature discriminability and adaptability. Additionally, we performed ablation experiments to substantiate the rationality of our approach. Our code is available at https://github.com/cuiyujie0113/LSW-NET.