Millimeter-level surface deformation detection method based on high-precision laser point cloud registration

doi:10.11947/j.AGCS.2026.20250549

Abstract

Abstract:

Addressing the challenges of high feature mismatch rates and poor robustness in laser point cloud registration for geological hazard monitoring, as well as difficulty in distinguishing subtle deformations from vegetation interference and environmental noise, this paper proposes a millimeter-level surface deformation detection method driven by coarse-to-fine high-precision laser point cloud registration. First, graph theory is used to eliminate gross errors in feature matching, and an enhanced GNC-Welsch robust estimator is designed to prevent coarse registration from falling into local minima. High-precision registration is then achieved using microstructure-based hybrid feature factors. A “precise extraction-multi-dimensional verification” strategy is proposed: feature analysis filters vegetation interference and extracts candidate deformation regions, while a multi-dimensional verification framework integrating geometric, statistical, and physical features eliminates false deformations. Simulation experiments show the method maintains RMSE of 0.52~0.61 mm across different deformation magnitudes, achieving F₁ scores of 86.11% and 95.39% at deformation magnitudes of 5 mm and 8 mm respectively, validating its effectiveness for millimeter-level deformation detection. Real slope experiments confirm the algorithm effectively rejects false deformation clusters and accurately identifies 15 mm ground subsidence and 22.6 mm ground uplift, validating its robustness and practicability in complex field environments.

Key words: point cloud registration, robust estimation, surface deformation detection

CLC Number:

P237

Pengcheng WEI, Guiyu JIANG, Hangyi SHEN, Haifeng HUANG, Rongling ZHANG. Millimeter-level surface deformation detection method based on high-precision laser point cloud registration[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(5): 866-880.

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d_max/m	分类性能			形变检测精度
d_max/m	精确度/（%）	召回率/（%）	F₁值/（%）	IoU/（%）	MAE/mm	RMSE/mm
0.005	100	75.60	86.11	75.60	0.33	0.52
0.006	100	83.68	91.11	83.68	0.33	0.52
0.007	100	88.77	94.05	88.77	0.34	0.52
0.008	100	91.19	95.39	91.19	0.34	0.52
0.01	100	91.49	95.56	91.49	0.33	0.52
0.02	100	96.45	98.19	96.45	0.35	0.54
0.05	100	98.49	99.24	98.49	0.33	0.53
0.1	100	98.39	99.19	98.39	0.35	0.61

聚类ID	点数	平均位移/mm	面积/m²	紧致度	位移标准差比例	梯度最大值	空间连续性	综合置信度/（%）	验证结果
S-0	895	－15.0	0.112	18.3	0.182	3.6	0.101	63.6	通过
S-1	707	－15.1	0.089	19.7	0.143	3.9	0.124	63.2	通过
S-2	646	－168.0	0.026	－	－	－	－	0	拒绝
U-0	810	17.3	0.095	21.5	0.217	6.8	0.068	54.3	拒绝
U-1	566	22.6	0.073	17.6	0.220	4.2	0.150	66.4	通过

统计指标	耗时/s	占比/（%）
预处理	14.52	45.8
粗配准	0.19	0.6
精配准	0.05	0.2
形变计算	16.77	52.9
多维验证	0.14	0.4
全流程总计	31.68	100

方法	配准重叠率/（%）	重叠区均值P2Pl/mm	初始形变点数	有效聚类数	有效形变点数
M1	47.21	5.06	3 115 409	－	－
M2	47.21	5.06	3 076 770	－	－
M3	47.21	5.06	3 076 770	35	40 218
M4	81.87	1.73	383 734	－	－
M5	81.87	1.73	129 011	3	2168