论文标题
imu denoing的几个射击域改编
Few-shot Domain Adaptation for IMU Denoising
论文作者
论文摘要
不同的应用程序场景将导致IMU表现出不同的误差特性，这会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情况设计。为了解决这个问题，我们提出了一些弹出域的适应方法。在这项工作中，考虑了一个域适应框架以降级IMU，重建损失旨在提高域的适应性。此外，为了在数据有限的情况下进一步提高适应性，采用了几次训练策略。在实验中，我们在两个数据集（Euroc和Tum-VI）和两个具有三种不同精度IMU的实际机器人（CAR和四倍的机器人）上量化了我们的方法。根据实验结果，T-SNE验证了我们框架的适应性。在取向结果中，我们提出的方法显示了出色的降解性能。
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.
