用气体传感器与惯性数据融合，实现机器人厘米级嗅觉定位。
Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation
- 通过校准气体传感器与惯性数据的同步关系，提升嗅觉导航精度。
- 在真实机械臂上验证，使臭源定位误差缩小至厘米级。
- 适用于医疗手术和无接触安检等高精度场景。
视觉惯性里程计（VIO）通过融合视觉与运动数据来理解机器人的状态。嗅觉惯性里程计（OIO）是其类比，将气体传感器信号与惯性数据融合，帮助机器人通过气味导航。气体动力学和环境因素会引入干扰，使OIO难以实现。本文提出一种适用于多种气体传感器类型的校准流程，重点实现慢速移动机器人平台在臭源定位中达到厘米级精度，以支持机器人手术和无接触安全检测等应用场景。我们在真实机械臂上验证了该校准方法，结果表明其显著优于冷启动条件下的嗅觉导航性能。
原文摘要 · Abstract (English)
Visual inertial odometry (VIO) is a process for fusing visual and kinematic data to understand a machine's state in a navigation task. Olfactory inertial odometry (OIO) is an analog to VIO that fuses signals from gas sensors with inertial data to help a robot navigate by scent. Gas dynamics and environmental factors introduce disturbances into olfactory navigation tasks that can make OIO difficult to facilitate. With our work here, we define a process for calibrating a robot for OIO that generalizes to several olfaction sensor types. Our focus is specifically on calibrating OIO for centimeter-level accuracy in localizing an odor source on a slow-moving robot platform to demonstrate use cases in robotic surgery and touchless security screening. We demonstrate our process for OIO calibration on a real robotic arm and show how this calibration improves performance over a cold-start olfactory navigation task.
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