By utilizing global navigation satellite system (GNSS) position and velocity measurements, the fusion between the GNSS and the inertial navigation system provides accurate and robust navigation information. When considering land vehicles,like autonomous ground vehicles,off-road vehicles or mobile robots,a GNSS-based heading angle measurement can be obtained and used in parallel to the position measurement to bound the heading angle drift. Yet, at low vehicle speeds (less than 2m/s) such a model-based heading measurement fails to provide satisfactory performance. This paper proposes GHNet, a deep-learning framework capable of accurately regressing the heading angle for vehicles operating at low speeds. We demonstrate that GHNet outperforms the current model-based approach for simulation and experimental datasets.

翻译：利用全球导航卫星系统（GNSS）的位置和速度测量，GNSS与惯性导航系统的融合能够提供精确且稳健的导航信息。对于陆地车辆，如自动驾驶地面车辆、越野车辆或移动机器人，可以获取基于GNSS的航向角测量值，并将其与位置测量值并行使用，以限制航向角漂移。然而，在低速（低于2m/s）行驶时，这种基于模型的航向测量方法无法提供满意的性能。本文提出GHNet，一种深度学习框架，能够精确回归低速运行车辆的航向角。我们证明，在仿真和实验数据集上，GHNet的性能优于当前基于模型的方法。