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<p>In this paper, a novel method for loop closure detection (LCD) is presented, which utilizes multi-layer occupied bird’s eye view (BEV) images. Initially, the original point clouds undergo preprocessing and are then projected into multi-layer occupied BEV images. Subsequently, to expedite the extraction of features from the BEV images, we employ a lightweight U-shaped network incorporating the Atrous Spatial Convolution Pooling Pyramid (ASPP) module. Lastly, a similarity measurement is devised, leveraging the rotation-invariance characteristic of the descriptor. The primary innovations center around the design of the lightweight U-shaped network, successfully achieved through the use of depth-wise separable convolution (DW-conv) and larger separable convolution kernels during the down-sampling process. Additionally, the incorporation of the ASPP module before the up-sampling process enriches the feature information derived from point clouds. Experimental comparisons conducted on the NCLT and MulRan datasets highlight significant improvements in both precision and efficiency achieved by the proposed method. Notably, the inference time is reduced by more than 20% when compared to the advanced DiSCO method. Furthermore, our LCD method has been seamlessly integrated into a comprehensive LiDAR-based simultaneous localization and mapping (SLAM) system, providing validation of its effectiveness.</p>

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A fast and robust 3D LiDAR-based loop closure detection method using BEV images

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Chizhou Zhang,
  • Mingsong Chen,
  • Guanqiang Wang,
  • Yongcheng Lin,
  • Zehao Li

摘要

In this paper, a novel method for loop closure detection (LCD) is presented, which utilizes multi-layer occupied bird’s eye view (BEV) images. Initially, the original point clouds undergo preprocessing and are then projected into multi-layer occupied BEV images. Subsequently, to expedite the extraction of features from the BEV images, we employ a lightweight U-shaped network incorporating the Atrous Spatial Convolution Pooling Pyramid (ASPP) module. Lastly, a similarity measurement is devised, leveraging the rotation-invariance characteristic of the descriptor. The primary innovations center around the design of the lightweight U-shaped network, successfully achieved through the use of depth-wise separable convolution (DW-conv) and larger separable convolution kernels during the down-sampling process. Additionally, the incorporation of the ASPP module before the up-sampling process enriches the feature information derived from point clouds. Experimental comparisons conducted on the NCLT and MulRan datasets highlight significant improvements in both precision and efficiency achieved by the proposed method. Notably, the inference time is reduced by more than 20% when compared to the advanced DiSCO method. Furthermore, our LCD method has been seamlessly integrated into a comprehensive LiDAR-based simultaneous localization and mapping (SLAM) system, providing validation of its effectiveness.