SCDCE-3D: Soft-Weighted Covariance and Dual-Branch Channel Enhancement for 3D Place Recognition in Complex Orchard Environments

Abstract

Recent progress in 3D place recognition has delivered strong results in urban and indoor scenarios, but orchards remain largely unexplored. In these environments, unreliable or absent GNSS signals necessitate LiDAR-based place recognition for robust long-term localization, yet challenges such as ill-defined geometry, semi-transparent foliage, and severe inter-/intra-row overlaps cause high structural ambiguity. To address these challenges, we propose SCDCE-3D, a novel framework that integrates soft-weighted covariance representation with dual-branch channel enhancement. The soft-weighted covariance module adaptively down-weights noisy or overlapping points using a sigmoid-based weighting strategy, enabling robust second-order statistical representation that suppresses cross-row interference. In parallel, a dual-branch backbone extracts complementary global and local features, which drive a dynamic channel enhancement mechanism to emphasize discriminative feature channels while suppressing redundancy. Furthermore, multi-level triplet learning is applied not only to the final descriptor but also to intermediate statistical features, reinforcing robustness against structural ambiguity. Experiments on orchard-based LiDAR datasets demonstrate that SCDCE-3D significantly outperforms state-of-the-art methods in both recall and robustness, offering a reliable solution for long-term 3D place recognition in agricultural robotics. Code is available at https://github.com/typist2001/SCDCE-3D.