用嗅觉+惯性数据实现机器人嗅觉导航，类比视觉惯性里程计。
Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
- 结合惯性运动与快速采样嗅觉传感器，构建嗅觉里程计算法
- 在5自由度机械臂上成功实现气味追踪任务
- 为农业和食品质检等场景提供可扩展的嗅觉导航基础
嗅觉导航是生物体最原始的探索机制之一。机器嗅觉（人工嗅觉）的模拟与实现极为困难。本文提出嗅觉惯性里程计（Olfactory Inertial Odometry, OIO），利用惯性运动学与快速采样嗅觉传感器，实现类视觉惯性里程计（VIO）的嗅觉导航。我们验证了SLAM与VIO原理可外推至嗅觉领域，支持真实世界机器人任务。在一台5-自由度机械臂上，通过三种不同的气味定位算法，在类农业与食品质量控制的应用场景中实现了气味追踪。结果表明，本工作建立了OIO的基准框架，未来可在此基础上进一步优化以应对更复杂任务。
原文摘要 · Abstract (English)
Olfactory navigation is one of the most primitive mechanisms of exploration used by organisms. Navigation by machine olfaction (artificial smell) is a very difficult task to both simulate and solve. With this work, we define olfactory inertial odometry (OIO), a framework for using inertial kinematics, and fast-sampling olfaction sensors to enable navigation by scent analogous to visual inertial odometry (VIO). We establish how principles from SLAM and VIO can be extrapolated to olfaction to enable real-world robotic tasks. We demonstrate OIO with three different odour localization algorithms on a real 5-DoF robot arm over an odour-tracking scenario that resembles real applications in agriculture and food quality control. Our results indicate success in establishing a baseline framework for OIO from which other research in olfactory navigation can build, and we note performance enhancements that can be made to address more complex tasks in the future.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。