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.

GPS 和 IMU 传感器融合在 GPS 无信号环境下提升自主车辆导航的可靠性和精度的方法。

可靠自动驾驶车辆位置估计的 GPS-IMU 传感器融合

GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position  Estimation

This paper details the design and implementation of a system for predicting
and interpolating object location coordinates. Our solution is based on
processing inertial measurements and global positioning system data through a
Long Short-Term Memory (LSTM) neural network and polynomial regression. LSTM is
a type of recurrent neural network (RNN) particularly suited for processing
data sequences and avoiding the long-term dependency problem. We employed data
from real-world vehicles and the global positioning system (GPS) sensors. A
critical pre-processing step was developed to address varying sensor
frequencies and inconsistent GPS time steps and dropouts. The LSTM-based
system's performance was compared with the Kalman Filter. The system was tuned
to work in real-time with low latency and high precision. We tested our system
on roads under various driving conditions, including acceleration, turns,
deceleration, and straight paths. We tested our proposed solution's accuracy
and inference time and showed that it could perform in real-time. Our
LSTM-based system yielded an average error of 0.11 meters with an inference
time of 2 ms. This represents a 76\% reduction in error compared to the
traditional Kalman filter method, which has an average error of 0.46 meters
with a similar inference time to the LSTM-based system.

该研究报告详细介绍了基于惯性测量和全球定位系统数据的长短期记忆 (LSTM) 神经网络和多项式回归的对象位置坐标的预测与插值系统的设计和实现。通过对真实世界车辆和全球定位系统传感器的数据处理，我们的 LSTM 系统在实时低延迟和高精度中展现了较传统的卡尔曼滤波方法更低的平均误差和类似的推断时间。