| 摘 要: 为了提升车辆定位系统的精度和鲁棒性,针对单一传感器存在的局限性,提出了一种基于图优化的LIDAR(Light Detection and Ranging,LIDAR)、IMU(Inertial Measurement Unit)和GNSS-RTK(Global Navigation Satellite System-Real-TimeKinematic)的多传感器车辆定位方法。首先,使用IMU预积分模型,通过滑动窗口和扫描匹配的方法构建LIDAR里程计因子,加入GNSS-RTK绝对测量值以修正系统的长期漂移;其次,使用因子图优化框架将LIDAR、IMU和GNSS-RTK的测量数据进行融合,并加入回环检测因子,通过求解最大后验估计以获取最佳的定位结果。实验结果显示,所提出方法的相对平移误差低至0.34m,具有较高的准确性和鲁棒性,弥补了单传感器的不足,提高了车辆定位系统的定位精度。 |
| 关键词: 图优化;多传感器融合;智能车辆;定位;回环检测 |
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中图分类号: TP242
文献标识码: A
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| 基金项目: 江苏省特种设备安全监督检验研究院科技计划项目(KJ(Y)2023042) |
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| Graph Optimization-Based Multi-Sensor Fusion Localization Method for Intelligent Vehicles |
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ZHANG Wei1, LI Xudong2, CAO Wei1, ZHAO Fengkui2
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(1.Jiangsu Special Equipment Safety Supervision Inspection Institute Wujiang Branch, Suzhou 215200, China;
2. College of Automobile and Traf fic Engineering, Nanjing Forestry University, Nanjing 210037, China)
smile3579@163.com; dalingbao@njfu.edu.cn; 634210329@qq.com; zfk@njfu.edu.cn
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| Abstract: To enhance the accuracy and robustness of vehicle localization systems and address the limitations of single-sensor approaches, this paper proposes a graph optimization-based multi-sensor fusion localization method integrating LIDAR (Light Detection and Ranging), IMU ( Inertial Measurement Unit), and GNSS-RTK (Global Navigation Satellite System-Real-Time Kinematic). Firstly, an IMU pre-integration model is employed to construct LIDAR odometry factors through sliding window and scan matching techniques, while GNSS-RTK absolute measurements are incorporated to correct long-term system drift. Subsequently, a factor graph optimization framework is utilized to fuse measurements from LIDAR, IMU, and GNSS-RTK, augmented with loop closure detection factors.The optimal localization results are obtained by solving the maximum a posteriori estimation. Experimental results demonstrate that the proposed method achieves a relative translation error as low as 0.34 m, exhibiting high accuracy and robustness. This approach effectively compensates for the shortcomings of single-sensor systems and significantly improves the positioning precision of vehicle localization systems. |
| Keywords: graph optimization; multi-sensor fusion; intelligent vehicles; location; loop detection |
