提出13种数据驱动方法，提升惯性传感器网络性能。
Enhancement of Neural Inertial Regression Networks: A Data-Driven Perspective
- 从架构、数据增强、预处理三方面改进神经网络
- 旋转+加噪的数据增强效果最显著，提升明显
- 适用于机器人、可穿戴设备等惯性感知场景
惯性传感器广泛应用于机器人及日常生活。近年来，深度学习显著提升了惯性传感的性能与鲁棒性。尽管深度学习在多个领域和平台中被用于提升网络表现，但缺乏统一基准进行公平比较与评估。为此，本文系统定义并分析了13种数据驱动的神经惯性回归网络优化技术，重点聚焦网络架构、数据增强与数据预处理三个维度。实验在六个不同来源的数据集上展开，涵盖四轴飞行器、门、行人及移动机器人等多种平台，共分析超过1079分钟、采样率120-200Hz的惯性数据。结果表明，通过旋转与噪声添加进行数据增强能持续带来最显著的性能提升。本研究还提出了可用于提升神经惯性回归网络的基准评测策略。
原文摘要 · Abstract (English)
Inertial sensors are integral components in numerous applications, powering crucial features in robotics and our daily lives. In recent years, deep learning has significantly advanced inertial sensing performance and robustness. Deep-learning techniques are used in different domains and platforms to enhance network performance, but no common benchmark is available. The latter is critical for fair comparison and evaluation in a standardized framework as well as development in the field. To fill this gap, we define and thoroughly analyze 13 data-driven techniques for improving neural inertial regression networks. A focus is placed on three aspects of neural networks: network architecture, data augmentation, and data preprocessing. Extensive experiments were made across six diverse datasets that were collected from various platforms including quadrotors, doors, pedestrians, and mobile robots. In total, over 1079 minutes of inertial data sampled between 120-200Hz were analyzed. Our results demonstrate that data augmentation through rotation and noise addition consistently yields the most significant improvements. Moreover, this study outlines benchmarking strategies for enhancing neural inertial regression networks.
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