Road pole-like object change detection supported by visual point cloud quality optimization

doi:10.11947/j.AGCS.2026.20250310

Abstract

Abstract:

Visual sensors are currently the most common perception sensors for crowdsourced road change detection. However, visual SLAM often suffers from limited accuracy and robustness in such applications. To address it, this study proposes a road pole-like object change detection technology framework supported by visual point cloud quality optimization. First, a visual point cloud optimization method is constructed by fusing semantic constraints, LiDAR point cloud depth, and GNSS global correction, significantly improving trajectory accuracy and point cloud quality. Second, based on the optimized visual point cloud, accurate extraction and localization of road pole-like objects are achieved. Then, a fast-matching strategy based on hash mapping is introduced to achieve robust change detection of pole-like objects across different periods. Finally, the effectiveness of the overall process is verified using two phases of experimental data collected around Tongji University. Experiments show that the proposed method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the trajectory by 68.39% and 65.65%, respectively, while increasing the point cloud density by an average of 57.97%. In the tasks of element localization and change detection, the mean localization error of pole-like objects in a single direction is 2~3 m, and the mean plane error is around 3.5 m. The matching accuracy of pole-like objects reaches 94.8%, and the accuracy and recall rates of change detection for added and removed pole-like objects both reach 100%. The results demonstrate the effectiveness and stability of the proposed method, confirm the application value of multi-source data fusion and optimization technology in road pole-like object change detection, and provide a reliable technical path for high-definition map change detection and intelligent management of road facilities.

Key words: multi-source data, visual point cloud, road pole-like objects, change detection

CLC Number:

P237

Haopeng HU, Hangbin WU, Shihao ZHAN, Zaihao WEN, Chun LIU. Road pole-like object change detection supported by visual point cloud quality optimization[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(2): 344-358.

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变化情况	前期有杆状物	后期有杆状物	前后有语义变化	检测方法
新增	否	是	否	在杆状物匹配阶段，若当前期目标的候选匹配集合为空，则标记为新增候选。通过核密度估计过滤因噪声或临时障碍物导致的误检
移除	是	否	否	移除检测需验证点云缺失区域的持续性，并确认底部结构是否匹配历史数据，避免误判
语义变化	是	是	否	对两期同一杆状物的语义标签进行一致性校验，确定变更候选集，随后通过试验设备采集的图像数据多重验证，排除遮挡或误检

期	采集时间	GNSS	Mono	采集周期/s	采集长度/m	图像数
D1	2024-10	√	√	240	3 243.21	2401
D2	2025-04	√	√	247	3 263.67	2409

期	ORB-SLAM2			本文方法			优化提升
期	MAE/m	RMSE/m	时间/s	MAE/m	RMSE/m	时间/s	MAE/（%）	RMSE/（%）
D1	5.798	5.440	694	1.625	1.762	952	71.97	67.60
D2	5.941	6.047	682	2.091	2.196	972	64.80	63.69

期	ORB-SLAM2点云数量	本文方法点云数量	优化提升/（%）
D1	156 486	250 213	59.89
D2	140 852	219 792	56.04

期	DynaSLAM			本文方法（仅语义优化）
期	MAE/m	RMSE/m	时间/s	MAE/m	RMSE/m	时间/s
D1	3.763	3.424	1029	2.082	2.201	798
D2	3.913	3.664	1033	2.461	2.515	811