A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition

• URL: http://arxiv.org/abs/2508.08917v1
• Date: Tue, 12 Aug 2025 13:12:48 GMT
• Title: A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition
• Authors: Jintao Cheng, Jiehao Luo, Xieyuanli Chen, Jin Wu, Rui Fan, Xiaoyu Tang, Wei Zhang,
• Abstract summary: LiDAR-based Place Recognition (LPR) remains a critical task in Embodied Artificial Intelligence (AI) and Autonomous Driving.<n>Existing approaches reduce place recognition to a Euclidean distance-based metric learning task, neglecting the feature space's intrinsic structures and intra-class variances.<n>We propose a novel cross-view network based on an innovative fusion paradigm to address these challenges.
• Score: 12.93382945887946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR-based Place Recognition (LPR) remains a critical task in Embodied Artificial Intelligence (AI) and Autonomous Driving, primarily addressing localization challenges in GPS-denied environments and supporting loop closure detection. Existing approaches reduce place recognition to a Euclidean distance-based metric learning task, neglecting the feature space's intrinsic structures and intra-class variances. Such Euclidean-centric formulation inherently limits the model's capacity to capture nonlinear data distributions, leading to suboptimal performance in complex environments and temporal-varying scenarios. To address these challenges, we propose a novel cross-view network based on an innovative fusion paradigm. Our framework introduces a pseudo-global information guidance mechanism that coordinates multi-modal branches to perform feature learning within a unified semantic space. Concurrently, we propose a Manifold Adaptation and Pairwise Variance-Locality Learning Metric that constructs a Symmetric Positive Definite (SPD) matrix to compute Mahalanobis distance, superseding traditional Euclidean distance metrics. This geometric formulation enables the model to accurately characterize intrinsic data distributions and capture complex inter-class dependencies within the feature space. Experimental results demonstrate that the proposed algorithm achieves competitive performance, particularly excelling in complex environmental conditions.

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