面向可靠自动驾驶汽车位置估计的GPS-IMU传感器融合
GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position Estimation
AI总结：
针对GPS在城市峡谷等环境信号衰减、IMU存在累积漂移的问题，提出基于无迹卡尔曼滤波的GPS-IMU传感器融合方法，经KITTI数据集验证可显著降低各轴定位误差，提升自动驾驶导航可靠性。
摘要：
全球定位系统（Global Positioning System，GPS）导航可提供覆盖全球的精准定位，是天空视野无遮挡的开阔区域中的可靠定位选择，但在室内空间和城市峡谷中可能出现信号衰减。相比之下，惯性测量单元（Inertial Measurement Units，IMU）由陀螺仪和加速度计组成，可提供加速度、旋转变化等相对运动信息。与GPS不同，IMU不依赖外部信号，因此在GPS拒止环境中具备实用性。不过，由于通过积分加速度计算速度和位置时误差会不断累积，IMU会随时间产生漂移。因此，融合GPS与IMU对于提升自动驾驶汽车导航系统的可靠性与精度至关重要，尤其是在GPS信号受损的环境中。为保障导航流畅性并克服两类传感器各自的局限，本研究提出的方法对GPS与IMU数据进行融合，该传感器融合采用无迹卡尔曼滤波（Unscented Kalman Filter，UKF）这一贝叶斯滤波技术。所设计的导航系统具备鲁棒性，可提供连续精准的定位，这对自动驾驶汽车的安全运行至关重要，尤其在GPS拒止环境中。本项目使用KITTI GNSS与IMU数据集开展实验验证，结果表明GNSS-IMU融合技术降低了仅使用GNSS数据的均方根误差（RMSE）：x轴、y轴、z轴的RMSE分别从13.214、13.284、13.363降至4.271、5.275、0.224。基于UKF的实验结果表明，在GPS拒止环境中结合两类传感器优势的GPS-IMU传感器融合技术，是提升自动驾驶汽车导航性能的极具潜力的方向。
英文摘要：
Global Positioning System (GPS) navigation provides accurate positioning with global coverage, making it a reliable option in open areas with unobstructed sky views. However, signal degradation may occur in indoor spaces and urban canyons. In contrast, Inertial Measurement Units (IMUs) consist of gyroscopes and accelerometers that offer relative motion information such as acceleration and rotational changes. Unlike GPS, IMUs do not rely on external signals, making them useful in GPS-denied environments. Nonetheless, IMUs suffer from drift over time due to the accumulation of errors while integrating acceleration to determine velocity and position. Therefore, fusing the GPS and IMU is crucial for enhancing the reliability and precision of navigation systems in autonomous vehicles, especially in environments where GPS signals are compromised. To ensure smooth navigation and overcome the limitations of each sensor, the proposed method fuses GPS and IMU data. This sensor fusion uses the Unscented Kalman Filter (UKF) Bayesian filtering technique. The proposed navigation system is designed to be robust, delivering continuous and accurate positioning critical for the safe operation of autonomous vehicles, particularly in GPS-denied environments. This project uses KITTI GNSS and IMU datasets for experimental validation, showing that the GNSS-IMU fusion technique reduces GNSS-only data's RMSE. The RMSE decreased from 13.214, 13.284, and 13.363 to 4.271, 5.275, and 0.224 for the x-axis, y-axis, and z-axis, respectively. The experimental result using UKF shows promising direction in improving autonomous vehicle navigation using GPS and IMU sensor fusion using the best of two sensors in GPS-denied environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
