用蛇形运动训练神经网络，减少惯性导航漂移。
Snake-Inspired Mobile Robot Positioning with Hybrid Learning
- 通过蛇形运动激发非线性行为，提升神经网络对移动距离的估计精度。
- 在290分钟实测数据上，定位误差比现有方法降低33%。
- 适合依赖惯性传感器的无人车、搜救机器人等场景使用。
移动机器人广泛应用于配送、搜救等领域。为实现精准导航，通常搭载多种传感器，但在真实环境中常仅依赖惯性传感器。由于惯性读数存在噪声和误差，导航结果会随时间漂移。为此，本文提出MoRPINet框架，利用神经网络回归机器人行进距离。通过让机器人执行蛇形滑动运动，激发非线性动态以增强学习效果。该方法在290分钟的实地实验数据上验证，相比其他先进纯惯性导航方法，定位误差降低了33%。
原文摘要 · Abstract (English)
Mobile robots are used in various fields, from deliveries to search and rescue applications. Different types of sensors are mounted on the robot to provide accurate navigation and, thus, allow successful completion of its task. In real-world scenarios, due to environmental constraints, the robot frequently relies only on its inertial sensors. Therefore, due to noises and other error terms associated with the inertial readings, the navigation solution drifts in time. To mitigate the inertial solution drift, we propose the MoRPINet framework consisting of a neural network to regress the robot's travelled distance. To this end, we require the mobile robot to maneuver in a snake-like slithering motion to encourage nonlinear behavior. MoRPINet was evaluated using a dataset of 290 minutes of inertial recordings during field experiments and showed an improvement of 33% in the positioning error over other state-of-the-art methods for pure inertial navigation.
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