摘要
为解决传统快速激光雷达惯性里程计(FAST-LIO)在全球定位系统(GPS)拒止环境中,因初始高度默认全局坐标系原点、Z轴观测约束单一,导致无人机定位精度,尤其是高度方向精度退化,进而制约其整体定位性能进一步提升的问题,提出低成本2D激光雷达与FAST-LIO集成的融合方案。方法上,先通过2D激光雷达完成极坐标转三维点云、随机采样一致性直线拟合、多重验证滤波及坐标转换,获取厘米级初始高度;然后将2D激光雷达与FAST-LIO自身的惯性测量单元、3D激光雷达结合,构建三重紧耦合系统;再将2D激光雷达观测融入迭代误差状态卡尔曼滤波(IESKF)观测矩阵,补充Z轴约束。该方法低成本易集成,有效提升无人机定位及位姿精度,支撑GPS拒止场景自主导航,未来将探索三维平面拟合优化适应性。
To address the issue of degraded positioning accuracy,particularly along the vertical axis,in traditional fast LiDAR-inertial odometry systems operating in GPS-denied environments—a problem stemming from the default initialization of height at the global coordinate origin and insufficient observational constraints in the Z-axis,which limits further improvement of overall localization performance—this paper proposes a fusion scheme that integrates a low-cost 2D LiDAR with the FAST-LIO framework.Methodologically,the approach begins by converting polar coordinate data from the 2D LiDAR into a 3D point cloud,followed by Random Sample Consensus line fitting,multi-stage validation filtering,and coordinate transformation to obtain a centimeter-level initial height estimate.Subsequently,a tightlycoupled system is constructed by combining the 2D Light detection and ranging(LiDAR)with the inherent inertial measurement unit(IMU)and 3D LiDAR of FAST-LIO.Observations from the 2D LiDAR are incorporated into the observation matrix of the iterated error state kalman filter(IESKF),thereby enhancing constraints in the Z-axis.The proposed method is low-cost,easy to integrate,and effectively improves the positioning and pose estimation accuracy of unmanned aerial vehicles(UAVs),supporting reliable autonomous navigation in GPS-denied scenarios.Future work will explore the use of 3D plane fitting to further optimize adaptability.
作者
赵威
严怀成
高生
吕云凯
ZHAO Wei;YAN Huaicheng;GAO Sheng;LÜYunkai(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《上海航天(中英文)》
2026年第1期114-124,共11页
Aerospace Shanghai(Chinese&English)
基金
国家自然科学基金资助项目(62333005)。
