论文/arXiv 2603.07582·27 天前

用于狗航位推算的基于模型和神经辅助方法

提出仅用惯性传感器的DDR三种算法，并在犬类与机器狗数据集上验证神经辅助方法优于模型方法。

AI 整理

• 数据与采集
• 评测与安全

原文链接：Model-Based and Neural-Aided Approaches for Dog Dead Reckoning（arXiv 2603.07582 · 发布日期：2026-10-01）

Model-Based and Neural-Aided Approaches for Dog Dead Reckoning

提出仅用惯性传感器的DDR三种算法，并在犬类与机器狗数据集上验证神经辅助方法优于模型方法。

核心要点

• 犬类应用和机器狗任务都面临惯性传感累计漂移导致的准确定位挑战。
• 提出三种仅依赖惯性传感器的定位算法，统称为狗航位推算DDR。
• 设计DogMotion可穿戴单元采集犬类数据，并结合机器狗数据集进行评估。
• 两类数据集上神经辅助方法持续优于模型方法，代码和数据集已公开。

验证范围与局限：验证限于13分钟DogMotion犬类数据和116分钟机器狗数据集。

英文摘要 (Abstract)

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 5%. 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.

作者：Gal Versano, Itai Savin, Itzik Klein

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2603.07582：Model-Based and Neural-Aided Approaches for Dog Dead Reckoning访问 2026-10-21