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<p>In simultaneous localization and mapping (SLAM), recognizing previously revisited locations, a task known as loop closure detection, is crucial for correcting accumulated drift and ensuring reliable navigation, especially in large-scale or multi-robot mapping scenarios with numerous keyframes. In LiDAR-based loop closure detection, a bird’s-eye-view (BEV) projection combined with a k-d tree is a widely used approach due to its efficiency. However, this combination can lead to significant information loss when compressing a 3D point cloud into a compact fixed-length global descriptor, which may exclude true loop candidates and result in missed loop closures. To address this limitation, we propose <i>DZLoop</i>, a novel LiDAR-based loop closure detection method that leverages multiple structural density images and Zernike moments. By combining these components, DZLoop generates a robust global descriptor that effectively captures diverse structural characteristics of the environment. Unlike conventional methods that obtain rotation invariance by compressing rows or columns of the BEV image, Zernike moments inherently provide image-level rotation invariance. Experimental evaluations on public datasets, including KITTI and HeLiPR, demonstrate that DZLoop outperforms existing methods such as M2DP, PALM, ScanContext, and NDD, achieving more reliable loop detection performance.</p>

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Lidar loop closure detection using density images and Zernike moments

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Doyeon Kim,
  • Heoncheol Lee

摘要

In simultaneous localization and mapping (SLAM), recognizing previously revisited locations, a task known as loop closure detection, is crucial for correcting accumulated drift and ensuring reliable navigation, especially in large-scale or multi-robot mapping scenarios with numerous keyframes. In LiDAR-based loop closure detection, a bird’s-eye-view (BEV) projection combined with a k-d tree is a widely used approach due to its efficiency. However, this combination can lead to significant information loss when compressing a 3D point cloud into a compact fixed-length global descriptor, which may exclude true loop candidates and result in missed loop closures. To address this limitation, we propose DZLoop, a novel LiDAR-based loop closure detection method that leverages multiple structural density images and Zernike moments. By combining these components, DZLoop generates a robust global descriptor that effectively captures diverse structural characteristics of the environment. Unlike conventional methods that obtain rotation invariance by compressing rows or columns of the BEV image, Zernike moments inherently provide image-level rotation invariance. Experimental evaluations on public datasets, including KITTI and HeLiPR, demonstrate that DZLoop outperforms existing methods such as M2DP, PALM, ScanContext, and NDD, achieving more reliable loop detection performance.