用卷积网络深度优化惯性导航误差，提升定位精度。
Evaluating the Impact of Convolutional Neural Network Layer Depth on the Enhancement of Inertial Navigation System Solutions
- 用监督学习的卷积神经网络修正惯性导航误差
- 深层网络比浅层更有效减少位置漂移
- 适合自动驾驶与无人机等高精度导航场景
安全导航对自动驾驶、机器人和航空等领域至关重要。惯性导航系统（INS）通过自推算估计位置、速度和姿态，尤其在无外部参考（如GPS）时依赖性强。然而，其内置的三轴加速度计和三轴陀螺仪易受零点偏置、量程误差和噪声影响，显著降低导航精度，成为系统关键弱点。本文采用监督式卷积神经网络（ConvNet）应对该问题，并系统评估网络层数深度对误差校正效果的影响。目标是确定最优网络结构，在最大化误差修正能力的同时实现高精度导航解算。
原文摘要 · Abstract (English)
Secure navigation is pivotal for several applications including autonomous vehicles, robotics, and aviation. The inertial navigation system estimates position, velocity, and attitude through dead reckoning especially when external references like GPS are unavailable. However, the three accelerometers and three gyroscopes that compose the system are exposed to various types of errors including bias errors, scale factor errors, and noise, which can significantly degrade the accuracy of navigation constituting also a key vulnerability of this system. This work aims to adopt a supervised convolutional neural network (ConvNet) to address this vulnerability inherent in inertial navigation systems. In addition to this, this paper evaluates the impact of the ConvNet layer's depth on the accuracy of these corrections. This evaluation aims to determine the optimal layer configuration maximizing the effectiveness of error correction in INS (Inertial Navigation System) leading to precise navigation solutions.
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