A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition
This addresses localization challenges for autonomous driving systems in GPS-denied environments, representing an incremental improvement over existing metric learning approaches.
The paper tackles the problem of LiDAR-based place recognition in GPS-denied environments by addressing limitations of Euclidean distance-based methods that neglect intrinsic feature structures. The proposed cross-view network with a pseudo-global fusion paradigm and Mahalanobis distance metric achieves competitive performance, especially in complex conditions.
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.