Data Collection and Alignment Algorithms for Data-driven Inertial Navigation

In addressing the dataset requirements for indoor inertial navigation technology based on deep learning, this paper proposes a comprehensive dataset construction and data preprocessing scheme which includes equipment preparation, data collection and data alignment. To meet the demands of deep learning for data size and distribution diversity, a detailed data collection strategy is introduced. For the acquired dataset, a robust data processing methodology is employed, involving time calibration, data alignment and coordinate system transformation to synchronize sensor data with groundtruth data. The dataset is then trained and tested on the Baseline network, followed by ablation experiments. The results show that the dataset constructed by the proposed method has the best trajectory reconstruction performance, while the small-size dataset that does not contain complex motion patterns has obvious drift and the dataset that does not use the proposed data alignment algorithm has a very poor performance. These outcomes validate the proposed scheme’s capability to enhance the performance of indoor inertial navigation models.