Smartphone-Assisted and Polar Coordinate Elevation Feature Map Matching-Based Airborne-Ground Point Cloud Registration

CHEN Maolin; WANG Xiyu; MA Wei; JI Cuicui; PAN Jianping

doi:10.13203/j.whugis20240348

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Smartphone-Assisted and Polar Coordinate Elevation Feature Map Matching-Based Airborne-Ground Point Cloud Registration

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Smartphone-Assisted and Polar Coordinate Elevation Feature Map Matching-Based Airborne-Ground Point Cloud Registration
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 6, Pages: 1204-1215(2026)
- 作者机构：
  
  1.重庆交通大学智慧城市学院，重庆，400074
  2.自然资源部地理国情监测重点实验室，湖北武汉，430079
  3.重庆交通大学山地城市时空信息重庆市重点实验室，重庆，400074
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240348
  CLC： P237
- Received：12 March 2025，
  
  Published：05 June 2026
- 稿件说明：
移动端阅览
陈茂霖,王溪雨,马威，等.基于智能手机辅助与极坐标高程特征图匹配的机载-地基点云配准[J].武汉大学学报（信息科学版）,2026,51(6):1204-1215.

CHEN Maolin,WANG Xiyu,MA Wei,et al.Smartphone-Assisted and Polar Coordinate Elevation Feature Map Matching-Based Airborne-Ground Point Cloud Registration[J].Geomatics and Information Science of Wuhan University,2026,51(6):1204-1215.
陈茂霖,王溪雨,马威，等.基于智能手机辅助与极坐标高程特征图匹配的机载-地基点云配准[J].武汉大学学报（信息科学版）,2026,51(6):1204-1215. DOI：10.13203/j.whugis20240348

CHEN Maolin,WANG Xiyu,MA Wei,et al.Smartphone-Assisted and Polar Coordinate Elevation Feature Map Matching-Based Airborne-Ground Point Cloud Registration[J].Geomatics and Information Science of Wuhan University,2026,51(6):1204-1215. DOI：10.13203/j.whugis20240348

摘要

不同传感器的扫描范围差异大、数据重叠区域少，机地点云配准存在地基点云在大范围机载扫描区域的定位问题和重叠区域点云利用率低的问题。为此，提出一种智能手机定位、定向信息辅助，并利用极坐标网格下高程特征匹配的机地点云配准方法。利用手机定位、定向数据实现地基点云在机载坐标系中的初始配准，以地基扫描中心的定位结果为虚拟扫描中心，在机载点云中构建极坐标网格，并筛选最高可视点，建立极坐标高程特征图。根据特征图的环形特征和网格特征构建适应度函数，以此为基础提出一种基于遗传算法的最佳虚拟扫描中心搜索方法，计算配准参数。为验证所提方法的可行性，选取9站设站激光点云和4条手持激光点云，与机载激光点云进行配准。结果表明，与现有算法相比，所提方法的配准时间降低了约60%，平均配准时间为3.48 min，最优精度达到了0.016 8 m，具有良好的精度和效率。

Abstract

Objectives

Due to significant differences in scanning ranges between different sensors and the limited overlap areas

registration of airborne and ground-based point clouds faces challenges in locating ground-based point clouds within large airborne scanning areas and in feature extraction in overlapping regions. Focusing on this problem

an airborne-ground point cloud registration method is proposed

integrating smartphone positioning and orientation data.

Methods

The method combines smartphone positioning and orientation information for coarse registration

and utilizes elevation feature matching under a polar coordinate grid for fine registration. Smartphone positioning and orientation data are used to set the virtual scanning center in the airborne coordinate system and then a polar coordinate grid is constructed for feature construction. The highest visible point in each subgrid is selected to establish a polar elevation feature map for the ground-based point cloud. Based on the subgrid and ring characteristics of the polar elevation feature map

a fitness function is constructed. A genetic algorithm-based method is introduced to search for the optimal virtual scanning center. By performing genetic operations on the airborne map set and identifying the highest fitness value

fine registration of the ground-based point cloud in the airborne coordinate system is achieved.

Results

To verify the feasibility of the proposed method

9 station-based point clouds and 4 handheld-based point clouds are registered with airborne laser point clouds. The results demonstrate that

despite differences in point cloud distribution caused by varying acquisition methods

the fitness variation curves for station-based and handheld point clouds show slight differences

but convergence is achieved within 300 iterations of the genetic algorithm. The final coarse registration accuracy difference does not exceed 0.03 m. Compared to existing algorithms

the registration time is reduced by approximately 60%

with an average registration time of 3.48 min and optimal accuracy reaching 0.016 8 m. Additionally

smartphone positioning and orientation accuracy were verified

showing that when positioning accuracy is within 50 m

the registration success rate exceeds 90%

and within 20 m

the success rate reaches over 98%.

Conclusions

Assistance from smartphone positioning and orientation data enables rapid initial registration of ground-based point clouds within large airborne scanning areas. The polar coordinate elevation feature map provides multiple features for low-overlap areas

facilitating the construction of a fitness function and enhancing algorithm robustness. This method can be applied to cross-dataset transfer in urban scenarios.

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