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.

翻译：移动机器人广泛应用于从物流配送到搜救任务等多个领域。机器人通常搭载多种传感器以实现精确导航，从而确保任务顺利完成。在实际应用场景中，由于环境限制，机器人往往仅能依赖其惯性传感器进行导航。然而，惯性测量值存在的噪声及其他误差项会导致导航解随时间发生漂移。为抑制惯性导航解的漂移，本文提出MoRPINet框架，该框架通过神经网络回归机器人的行进距离。为实现这一目标，我们要求移动机器人以蛇形蜿蜒运动方式行进，以激发非线性行为特征。通过野外实验采集的290分钟惯性数据对MoRPINet进行评估，结果表明：在纯惯性导航任务中，该框架相较于其他先进方法将定位误差降低了33%。