Different application scenarios will cause IMU to exhibit different error characteristics which will cause trouble to robot application. However, most data processing methods need to be designed for specific scenario. To solve this problem, we propose a few-shot domain adaptation method. In this work, a domain adaptation framework is considered for denoising the IMU, a reconstitution loss is designed to improve domain adaptability. In addition, in order to further improve the adaptability in the case of limited data, a few-shot training strategy is adopted. In the experiment, we quantify our method on two datasets (EuRoC and TUM-VI) and two real robots (car and quadruped robot) with three different precision IMUs. According to the experimental results, the adaptability of our framework is verified by t-SNE. In orientation results, our proposed method shows the great denoising performance.

翻译：不同的应用设想方案将使IMU呈现出不同的错误特性,从而给机器人应用带来麻烦。 但是,大多数数据处理方法都需要针对特定的假设方案设计。 为了解决这个问题,我们建议了几发域适应方法。 在这项工作中,考虑用一个域适应框架来取消IMU, 重新配置损失的目的是提高域的适应性。此外,为了进一步提高在有限数据情况下的适应性,还采用了一个几发训练战略。在实验中,我们用三种不同精确的IMU对两个数据集(EuRoC和TUM-VI)和两个真正的机器人(汽车和四重机器人)的方法进行了量化。根据实验结果,我们的框架的适应性得到了 t-SNE的验证。在定向结果中,我们提出的方法显示了巨大的解密性能。