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.

翻译：全球定位系统（GPS）导航具有全球覆盖精度，在天空视野开阔的区域能提供可靠的定位。然而，在室内空间和城市峡谷中，信号可能发生衰减。相比之下，惯性测量单元（IMU）由陀螺仪和加速度计构成，可提供加速度和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拒止环境中具有重要应用价值。但IMU在通过加速度积分推算速度和位置时会产生累积误差，导致随时间推移出现漂移现象。因此，融合GPS与IMU对于提升自动驾驶车辆导航系统的可靠性与精度至关重要，尤其在GPS信号受损环境中。本文提出的方法通过融合GPS与IMU数据实现平滑导航，并克服各传感器的局限性。该传感器融合采用无迹卡尔曼滤波（UKF）贝叶斯滤波技术。所设计的导航系统具有鲁棒性，能在GPS拒止环境下为自动驾驶车辆安全运行提供连续精准的定位。本项目使用KITTI GNSS与IMU数据集进行实验验证，结果表明GNSS-IMU融合技术可显著降低仅使用GNSS数据的均方根误差（RMSE）。在x轴、y轴、z轴上，RMSE分别从13.214、13.284、13.363降至4.271、5.275、0.224。采用UKF的实验结果为在GPS拒止环境中通过GPS与IMU传感器融合发挥两种传感器优势来改进自动驾驶车辆导航展现了有前景的方向。