用惯性传感器实现犬类精确定位，误差低于10%。
Model-Based and Neural-Aided Approaches for Dog Dead Reckoning
- 结合模型与神经网络，仅靠惯性传感器实现定位
- 在两个数据集上均实现小于10%的绝对距离误差
- 适合生物犬与机器人狗的低成本高精度定位
现代犬类应用涵盖医疗与服务领域，而仿生腿式机器人狗则用于高风险工业巡检、灾害响应及搜救任务。由于惯性传感固有的累积漂移，精确定位仍是重大挑战。为此，我们提出三种仅使用惯性传感器的定位算法，统称为犬类死记里程（DDR）。为评估方法，我们设计了可穿戴的DogMotion设备，采集了13分钟的犬类数据；同时使用时长116分钟的机器人腿式狗数据集。在两个不同数据集上，我们的神经网络辅助方法持续优于纯模型方法，绝对距离误差低于10%。因此，我们提供了一种轻量且低成本的生物犬与机器人狗定位解决方案。为支持可复现性，代码与数据集已公开。
原文摘要 · Abstract (English)
Modern canine applications span medical and service roles, while robotic legged dogs serve as autonomous platforms for high-risk industrial inspection, disaster response, and search and rescue operations. For both, accurate positioning remains a significant challenge due to the cumulative drift inherent in inertial sensing. To bridge this gap, we propose three algorithms for accurate positioning using only inertial sensors, collectively referred to as dog dead reckoning (DDR). To evaluate our approaches, we designed DogMotion, a wearable unit for canine data recording. Using DogMotion, we recorded a dataset of 13 minutes. Additionally, we utilized a robotic legged dog dataset with a duration of 116 minutes. Across the two distinct datasets we demonstrate that our neural-aided methods consistently outperform model-based approaches, achieving an absolute distance error of less than 10\%. Consequently, we provide a lightweight and low-cost positioning solution for both biological and legged robotic dogs. To support reproducibility, our codebase and associated datasets have been made publicly available.
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