计算机应用与软件2025，Vol.42Issue(5)：78-87,10.DOI:10.3969/j.issn.1000-386x.2025.05.012
基于多传感器紧耦合的车辆状态并行估计方法及建图系统
PARALLEL VEHICLE STATE ESTIMATION METHOD AND MAPPING SYSTEM BASED ON TIGHTLY COUPLED MULTI-SENSOR FUSION
杜俊文 1敖银辉 1宋学佳 1舒诚龙1
作者信息
- 1. 广东工业大学机电工程学院 广东 广州 510006
- 折叠
摘要
Abstract
Using multi-sensor fusion technology can make the simultaneous location and mapping(SLAM)system obtain better performance.The traditional Lidar positioning system will produce positioning drift or system failure in scenes with sparse features.For solving the problems above,a parallel multi-sensor fusion system LIGNS based on iterative error state Kalman filter(IESKF[1])theory is designed.Different sensors were used to update vehicle state estimation independently and in real time.LIGNS integrated Lidar,IMU and dual antenna GNSS equipment that could additionally provide heading measurement.LIGNS removed the ground point cloud through a two-step filter method,and extracted the features.The features were saved in the slide window to make the feature point cloud denser to deal with the scene with sparse features.The experimental results show that LIGNS can achieve high-precision positioning and mapping,and has better real-time performance.关键词
多传感器融合/状态估计/误差状态卡尔曼滤波/SLAMKey words
Multi-sensor fusion/State estimation/Error-state Kalman filter/SLAM分类
信息技术与安全科学引用本文复制引用
杜俊文,敖银辉,宋学佳,舒诚龙..基于多传感器紧耦合的车辆状态并行估计方法及建图系统[J].计算机应用与软件,2025,42(5):78-87,10.
