基于3D高斯溅射的大规模场景重定位方法

张守建; 刘皓泽; 陆海涛; 陈海吉尔; 徐博; 赵磊

doi:10.13203/j.whugis20250276

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基于3D高斯溅射的大规模场景重定位方法

大地测量学与测量工程 | 更新时间：2026-11-05

- 基于3D高斯溅射的大规模场景重定位方法
- Large-Scale Scene Relocation via 3D Gaussian Splatting in Urban Enviroment
- 武汉大学学报（信息科学版） 2026年51卷第4期页码：689-702
- 作者机构：
  
  1.武汉大学测绘学院，湖北武汉，430079
  2.武汉大学珞珈实验室，湖北武汉，430079
- 作者简介：
  
  张守建，博士，副教授，主要从事卫星、机器人导航领域研究。shjzhang@sgg.whu.edu.cn
  徐博，博士，副研究员。boxu1995@whu.edu.cn
- 基金信息：
  
  湖北省重大攻关项目（JD）(2023BAA025);国家自然科学基金面上项目(42374016)
- DOI：10.13203/j.whugis20250276
  中图分类号： P228
- 收稿：2025-09-20，
  
  纸质出版：2026-04-05
- 稿件说明：
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张守建,刘皓泽,陆海涛，等.基于3D高斯溅射的大规模场景重定位方法[J].武汉大学学报（信息科学版）,2026,51(4):689-702.

ZHANG Shoujian,LIU Haoze,LU Haitao,et al.Large-Scale Scene Relocation via 3D Gaussian Splatting in Urban Enviroment[J].Geomatics and Information Science of Wuhan University,2026,51(4):689-702.
张守建,刘皓泽,陆海涛，等.基于3D高斯溅射的大规模场景重定位方法[J].武汉大学学报（信息科学版）,2026,51(4):689-702. DOI：,10.13203/j.whugis20250276

ZHANG Shoujian,LIU Haoze,LU Haitao,et al.Large-Scale Scene Relocation via 3D Gaussian Splatting in Urban Enviroment[J].Geomatics and Information Science of Wuhan University,2026,51(4):689-702. DOI：,10.13203/j.whugis20250276

摘要

为了获取复杂环境下精确位姿信息，提出一种利用3D高斯溅射技术（3D Gaussian splatting，3DGS）的大规模重定位框架，使用单目相机实现厘米级定位。首先，利用多传感器数据构建城市复杂环境高精度3DGS地图，然后，利用基于深度学习的特征点提取与匹配方法和的视觉前端结合，实现了高效、鲁棒的特征点提取和匹配，最后通过求解3D到2D点对的方法，迭代渲染优化得到精确的位姿信息。在开源街景数据集KITTI上选取3种场景道路对所提方法进行了测试，实验结果表明，在城镇道路、林荫大道和交通密集型公路上的平均定位精度分别为0.026 m、0.029 m和0.081 m，优于其他常规方法。

Abstract

Objectives

In the field of autonomous driving

acquiring high-precision pose information is a critical factor in ensuring its safety and reliability. Although global navigation satellite system (GNSS) can provide absolute pose references

its positioning accuracy significantly degrades in complex scenarios due to signal obstruction and multipath effects

making it difficult to meet the stringent requirements of autonomous driving systems. To address this challenge

we have conducted systematic exploration into precise pose acquisition in large-scale

complex

and dynamic environments.

Methods

First

based on multi-sensor fusion technology

we developed a reconstruction system tailored for dynamic urban environments

focusing on three representative large-scale outdoor scenarios: urban roads

tree-lined avenues

and high-traffic highways. A high-precision 3D Gaussian splatter (3DGS) map was constructed for these scenarios. In the visual front-end

we integrated the SuperPoint feature extraction algorithm with the SuperGlue feature matching algorithm to achieve high-quality feature point detection and matching. Additionally

we introduced the perspective-

-point (P

P) algorithm

which estimates the initial camera pose by matching feature points with corresponding points in the 3D map.To optimize positioning results and improve the system's real-time performance

we proposed an iterative optimization strategy based on progressive rendering. This strategy utilizes the geometric structure and radiometric field characteristics of the 3DGS map

combining step-by-step rendering and optimization to continuously adjust the camera pose estimation

ultimately achieving progressive optimization of real-time high-precision positioning results.

Results

The experimental validation further demonstrates the following results: (1) In dynamic urban environments

the reconstruction system exhibits exceptional performance

effectively rendering continuous and realistic lighting and texture effects in real-time. Particularly in complex urban settings

the system handles issues such as lighting changes and occlusions caused by dynamic objects effectively. (2) The feature extraction and matching algorithms

SuperPoint and SuperGlue

significantly outperform traditional methods in terms of both the quantity and quality of feature points. Compared to traditional methods

the number of extracted feature points has been greatly increased

with a feature point distribution rate reaching 70%

more than twice that of conventional methods. (3) The system demonstrates excellent positioning accuracy in three typical outdoor scenarios. On urban roads

tree-lined avenues

and high-traffic highways

the system achieves average positioning accuracies of 0.026 m

0.029 m

and 0.081 m

respectively

which are significantly better than other representative methods. Additionally

the system achieves a frequency of 96% for absolute displacement and rotation errors smaller than 10 cm and 1°

respectively

indicating high stability in positioning accuracy and the ability to provide reliable real-time positioning results in various environments. (4) Ablation experiments further validate the effectiveness of the system's design. The complete model achieves the best results in both typical scenarios

with average absolute translation and rotation errors of 3.22 cm and 0.69°

respectively. Compared to experiments where dynamic occlusion handling

SuperPoint feature extraction

and iterative optimization strategies were removed

the system's overall design demonstrates superior performance. (5) The system's real-time performance has been verified. Time analysis shows that the system processes each frame of images in approximately 1 s

which meets the real-time requirements and is suitable for real-time application scenarios.

Conclusions

In large-scale outdoor environments

the visual relocalization technology based on the 3DGS map enables high-precision location information retrieval

effectively compensating for the limitations of existing positioning methods under GNSS signal occlusion or in complex environments. Through multi-sensor fusion and precise map construction

the system performs exceptionally well in dynamic urban scenarios. Particularly in complex urban environments

the issues of lighting changes and dynamic object occlusion are effectively handled

significantly enhancing the system's robustness and adaptability. The front-end feature extraction and matching methods greatly improve the system's stability and accuracy. In complex dynamic scenes

the distribution of feature points is more uniform

further enhancing positioning precision. Compared to various methods

the system demonstrates optimal performance. Ablation experiments further validate the necessity and effectiveness of these techniques. The system's processing speed meets real-time requirements

ensuring efficient and reliable real-time localization.

关键词

Keywords

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