用轮装惯性传感器+周期轨迹，66%降低定位漂移。
WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning
- 将惯性传感器装在轮子上，结合周期运动抑制误差积累。
- 在190分钟数据上实现比顶尖方法高66%的定位精度。
- 适合无卫星信号的室内外短时纯惯性导航场景。
自主移动机器人广泛应用于室内外导航、运输和巡检任务。在卫星信号受限或光照条件差的实际场景中，导航仅依赖惯性传感器，但由此导致的测量误差会引发定位快速漂移。本文提出WMINet，一种基于轮装惯性传感器的深度学习定位方法，仅依靠惯性数据实现机器人位置估计。为此，融合了轮装布置与周期性轨迹驱动两种实用策略以抑制惯性漂移，并引入轮距约束进一步提升定位性能。为评估该方法，我们使用Rosbot-XL采集了总计190分钟的轮装初始数据集，已公开。实验表明，该方法相较现有最优方案定位精度提升66%，显著改善了纯惯性环境下的导航能力，支持在复杂环境下实现短时无缝惯性导航。
原文摘要 · Abstract (English)
Autonomous mobile robots are widely used for navigation, transportation, and inspection tasks indoors and outdoors. In practical situations of limited satellite signals or poor lighting conditions, navigation depends only on inertial sensors. In such cases, the navigation solution rapidly drifts due to inertial measurement errors. In this work, we propose WMINet a wheel-mounted inertial deep learning approach to estimate the mobile robot's position based only on its inertial sensors. To that end, we merge two common practical methods to reduce inertial drift: a wheel-mounted approach and driving the mobile robot in periodic trajectories. Additionally, we enforce a wheelbase constraint to further improve positioning performance. To evaluate our proposed approach we recorded using the Rosbot-XL a wheel-mounted initial dataset totaling 190 minutes, which is made publicly available. Our approach demonstrated a 66\% improvement over state-of-the-art approaches. As a consequence, our approach enables navigation in challenging environments and bridges the pure inertial gap. This enables seamless robot navigation using only inertial sensors for short periods.
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