2025

Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues
Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues

Xianghong Zou, Jianping Li†, Weitong Wu, Fuxun Liang, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 10.6) 2025

We propose a LiDAR-based reliable global localization method, Reliable-loc, which achieves better robustness in complex large-scale outdoor scenes with insufficient features and incomplete coverage of the prior map. The experimental results indicate that Reliable-loc exhibits high robustness, accuracy, and efficiency in large-scale, complex street scenes, with a position accuracy of ±2.91 m, yaw accuracy of ±3.74 degrees, and achieves real-time performance.

[Project] [Papar] [Code]

Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues
Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues

Xianghong Zou, Jianping Li†, Weitong Wu, Fuxun Liang, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 10.6) 2025

We propose a LiDAR-based reliable global localization method, Reliable-loc, which achieves better robustness in complex large-scale outdoor scenes with insufficient features and incomplete coverage of the prior map. The experimental results indicate that Reliable-loc exhibits high robustness, accuracy, and efficiency in large-scale, complex street scenes, with a position accuracy of ±2.91 m, yaw accuracy of ±3.74 degrees, and achieves real-time performance.

[Project] [Papar] [Code]

2023

PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes
PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes

Xianghong Zou, Jianping Li†, Yuan Wang, Fuxun Liang, Weitong Wu, Haiping Wang, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 12.7) 2023

We propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (PCPR) tasks. The comprehensive experimental results indicate that PatchAugNet achieves SOTA performance with 83.43% recall@top1% and 60.34% recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by +9.57 recall@top1% and +15.50 recall@top1, while exhibiting better generalizability.

[Project] [Paper] [Code]

PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes
PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes

Xianghong Zou, Jianping Li†, Yuan Wang, Fuxun Liang, Weitong Wu, Haiping Wang, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 12.7) 2023

We propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (PCPR) tasks. The comprehensive experimental results indicate that PatchAugNet achieves SOTA performance with 83.43% recall@top1% and 60.34% recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by +9.57 recall@top1% and +15.50 recall@top1, while exhibiting better generalizability.

[Project] [Paper] [Code]

MuCoGraph: A Multi-scale Constraint Enhanced Pose Graph Framework for MLS Point Cloud Inconsistency Correction
MuCoGraph: A Multi-scale Constraint Enhanced Pose Graph Framework for MLS Point Cloud Inconsistency Correction

Yuhao Li Zou, Xianghong Zou, Tian Li, Sihan Sun, Yuan Wang, Fuxun Liang, Jianping Li†, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 12.7) 2023

We present MuCoGraph, which introduces multi-scale constraints to establish the correct correspondences for revisited areas, and formulates an enhanced pose graph for position inconsistency correction. The proposed method was used to correct the MLS point cloud position inconsistency in three datasets. The average three-dimensional distance of the checkpoints was reduced from 0.362 m, 0.108 m, and 1.027 m to 0.057 m, 0.033 m, and 0.051 m for datasets I, II, and III respectively. In addition, the root-mean-square error of all three datasets was less than 0.04 m after correction. The experiments confirmed that the proposed method can automatically locate and correct the position inconsistency of MLS point clouds, showing good robustness and effectiveness.

[Paper]

MuCoGraph: A Multi-scale Constraint Enhanced Pose Graph Framework for MLS Point Cloud Inconsistency Correction
MuCoGraph: A Multi-scale Constraint Enhanced Pose Graph Framework for MLS Point Cloud Inconsistency Correction

Yuhao Li Zou, Xianghong Zou, Tian Li, Sihan Sun, Yuan Wang, Fuxun Liang, Jianping Li†, Bisheng Yang†, Zhen Dong

ISPRS Journal of Photogrammetry and Remote Sensing (IF: 12.7) 2023

We present MuCoGraph, which introduces multi-scale constraints to establish the correct correspondences for revisited areas, and formulates an enhanced pose graph for position inconsistency correction. The proposed method was used to correct the MLS point cloud position inconsistency in three datasets. The average three-dimensional distance of the checkpoints was reduced from 0.362 m, 0.108 m, and 1.027 m to 0.057 m, 0.033 m, and 0.051 m for datasets I, II, and III respectively. In addition, the root-mean-square error of all three datasets was less than 0.04 m after correction. The experiments confirmed that the proposed method can automatically locate and correct the position inconsistency of MLS point clouds, showing good robustness and effectiveness.

[Paper]