测绘通报 ›› 2025, Vol. 0 ›› Issue (11): 124-128.doi: 10.13474/j.cnki.11-2246.2025.1119

• 学术研究 • 上一篇    下一篇

三维高斯溅射技术驱动的无人车实时定位和高保真建图算法

卢志强1, 庞清凯2, 魏舰3   

  1. 1. 无锡职业技术大学, 江苏 无锡 214121;
    2. 江苏大学汽车与交通工程学院, 江苏 镇江 212013;
    3. 浙江吉利控股集团有限公司, 浙江 杭州 310000
  • 收稿日期:2025-02-18 发布日期:2025-12-04
  • 作者简介:卢志强(1982—),男,硕士,实验师,主要研究方向为汽车工程等。E-mail:luzq198212@163.com
  • 基金资助:
    国家自然科学基金(52072157)

Real-time localization and high-fidelity mapping algorithm for unmanned vehicles driven by 3D Gaussian splatting technology

LU Zhiqiang1, PANG Qingkai2, WEI Jian3   

  1. 1. Wuxi University of Technical, Wuxi 214121, China;
    2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China;
    3. Zhejiang Geely Holding Group Co., Ltd., Hangzhou 310000, China
  • Received:2025-02-18 Published:2025-12-04

摘要: 针对神经渲染与SLAM结合方法在相机定位与高保真重建中计算成本高、效率低的问题,本文提出了一种基于三维高斯溅射(3D GS)的无人车SLAM算法,通过显式几何表达提升定位效率,并融合隐式表示学习光照与纹理,以实现高精度复杂场景还原。该方法采用基于高斯金字塔的多层次训练策略,以增强多尺度细节捕捉能力。试验结果表明,本文算法在多个数据集上性能优异,在Replica数据集上PSNR指标提升30%,验证了其高效性与重建质量。

关键词: 三维高斯溅射, 相机定位, 高保真重建, 无人车, SLAM

Abstract: To address the high computational cost and low efficiency of combining neural rendering with SLAM for camera localization and high-fidelity reconstruction,this paper proposes a SLAM algorithm for unmanned vehicles based on 3D Gaussian splatting(3D GS). This algorithm improves localization efficiency through explicit geometric representation and integrates implicit representations to learn illumination and texture to achieve high-precision reconstruction of complex scenes.This method employs a multi-level training strategy based on a Gaussian pyramid to enhance the ability to capture multi-scale details.Experimental results demonstrate excellent performance on multiple datasets,with a 30% improvement in PSNR on the Replica dataset,validating its efficiency and reconstruction quality.

Key words: 3D Gaussian splatting, camera positioning, high-fidelity reconstruction, autonomous vehicles, SLAM

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