上海交通大学学报

上海交通大学学报

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基于点云指纹的定位与建图方法

  

  1. 1. 北京航空航天大学 空间与环境学院，北京 100191；2. 国防科技大学 前沿交叉学科学院，长沙 410073；3. 国防科技大学 电子科学学院，长沙 410073；4. 大连理工大学 建设工程学院，辽宁 大连 116024
  • 作者简介:李飞（1993—），硕士，科研工程师，从事智能体高精定位研究.
  • 基金资助:

    国家自然科学基金（U20A20193）

FLAM: Point Cloud Fingerprint-Based Localization and Mapping

  1. 1. School of Space and Environment, Beihang University, Beijing 100191, China；2. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China；3. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China；4. School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China

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摘要： 为提升基于激光雷达的智能车定位性能，引入激光点云指纹的概念，并提出基于点云指纹的地图表征模型与智能车定位方法。基于点云极化与主成分特征的点云指纹表征，融合了点云的全局与局部描述。地图表征模型由序列节点构成，每个节点仅包含激光点云指纹与全局位姿信息。基于点云指纹地图的智能车多尺度定位被解耦成3个模块：基于普通全球导航卫星系统或轨迹预测的粗定位；利用点云指纹与基于向量的皮尔逊相关系数的节点定位；基于点云指纹与快速广义迭代最近点的度量定位。实验在公开的KITTI数据集与本地数据集下进行，所提出的方法取得了超过99%的地图匹配精度与高达7 cm的定位精度。结果表明，所提出的方法在不同场景下均取得了厘米级的高精定位，鲁棒且泛化性好。

关键词: 点云指纹, 极化表征, 主成分特征, 地图表征, 多尺度定位

Abstract:

To enhance the localization performance of intelligent vehicles using light detection and ranging (LiDAR), the concept of point cloud fingerprints is introduced, along with a map representation model and a localization method based on point cloud fingerprints. The point cloud fingerprint representation relies on point cloud polarization and principal component features, integrating global and local descriptions of the point cloud. The map representation model consists of sequential nodes containing only point cloud fingerprints and global poses. The multiscale localization of intelligent vehicles based on point cloud fingerprint maps is decoupled into three modules: coarse localization based on standard global navigation satellite system (GNSS) and trajectory prediction, node localization based on point cloud fingerprints and the vector-based Pearson correlation coefficient (RV), and metric localization based on point cloud fingerprints and the fast generalized iterative closest point (Fast-GICP). Experiments conducted on the publicly available KITTI dataset and a local dataset demonstrate that the proposed method achieves over 99% map-matching accuracy and up to 7 cm localization accuracy. The results indicate that the proposed method attains centimeter-level high-precision localization in various scenarios, with strong robustness and generalization capabilities.

Key words: point cloud fingerprint, polarization representation, principal component feature, map representation, multiscale localization

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