基于三维重建先验的无人车实时稠密SLAM算法

doi:10.13474/j.cnki.11-2246.2025.1013

测绘通报 ›› 2025, Vol. 0 ›› Issue (10): 76-81.doi: 10.13474/j.cnki.11-2246.2025.1013

• 学术研究 • 上一篇

基于三维重建先验的无人车实时稠密SLAM算法

张宏伟¹, 吕云飞¹, 高海宽¹, 杨鹏鑫¹, 吴文俊², 欧伟铭³

1. 河北石油职业技术大学汽车工程系, 河北承德 067000;
2. 吉林大学汽车仿真与控制国家重点实验室, 吉林长春 130025;
3. 比亚迪汽车工业有限公司汽车工程研究院, 广东深圳 518118

收稿日期:2025-03-11 发布日期:2025-10-31
通讯作者: 吕云飞。E-mail:llovr@163.com
作者简介:张宏伟(1972-),男,高级工程师,主要研究方向为汽车电控、自动驾驶预期功能安全、功能安全研究等。E-mail:zhanghw197207@163.com
基金资助:
河北省高等学校科学技术研究项目(ZC2023081)

Real-time dense SLAM algorithm for autonomous vehicles based on 3D reconstruction priors

ZHANG Hongwei¹, Lü Yunfei¹, GAO Haikuan¹, YANG Pengxin¹, WU Wenjun², OU Weiming³

1. Hebei Petroleum University of Technology, department of automotive engineering, Chengde 067000, China;
2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China;
3. Automotive Engineering Research Institute, BYD Automotive Industry Co., Ltd., Shenzhen 518118, China

Received:2025-03-11 Published:2025-10-31

摘要/Abstract

摘要： 针对无人车面临的复杂环境中准确定位与稠密建图的挑战,本文提出一种基于三维重建先验的实时单目稠密SLAM算法。通过引入稳健几何先验,在非结构化环境中展现出卓越的稳健性,且无需依赖预设相机模型,可以应对各种通用时变的相机模型。算法架构包含4个核心模块:点图匹配、跟踪与局部融合、图构建与闭环检测、二阶全局优化机制。经参数自适应标定,该算法在动态光照、弱纹理等复杂场景下的多类基准测试中达到领先性能,且可以达到实时运行的水平。

关键词: 无人车, 三维重建, 准确定位, 稠密建图, SLAM

Abstract: In response to the challenges faced by autonomous vehicles in achieving accurate localization and dense mapping in complex environments, this paper proposes a real-time monocular dense SLAM algorithm based on 3D reconstruction priors.By incorporating robust geometric priors, the algorithm demonstrates exceptional robustness in unstructured environments and does not rely on predefined camera models, making it adaptable to various general time-varying camera models.The algorithm's architecture consists of four core modules:point-to-map matching, tracking and local fusion, map construction and loop closure detection, a second-order global optimization mechanism.Through adaptive parameter calibration, the algorithm achieves leading performance in multiple benchmark tests under complex scenarios such as dynamic lighting and weak textures.Additionally, the algorithm is capable of operating in real-time.

Key words: autonomous vehicle, 3D reconstruction, accurate localization, dense mapping, SLAM

中图分类号:

P237

张宏伟, 吕云飞, 高海宽, 杨鹏鑫, 吴文俊, 欧伟铭. 基于三维重建先验的无人车实时稠密SLAM算法[J]. 测绘通报, 2025, 0(10): 76-81.

ZHANG Hongwei, Lü Yunfei, GAO Haikuan, YANG Pengxin, WU Wenjun, OU Weiming. Real-time dense SLAM algorithm for autonomous vehicles based on 3D reconstruction priors[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 76-81.

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基于三维重建先验的无人车实时稠密SLAM算法

Real-time dense SLAM algorithm for autonomous vehicles based on 3D reconstruction priors

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