Few-shot Domain Adaptation for IMU Denoising
This addresses domain adaptation for IMU denoising in robotics, but it appears incremental as it builds on existing domain adaptation techniques with a few-shot strategy.
The paper tackles the problem of IMU denoising across different application scenarios by proposing a few-shot domain adaptation method, achieving great denoising performance in orientation results as verified on datasets and real robots.
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.