Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation
This work addresses sensor calibration challenges for OIO in niche applications like robotic surgery and touchless security screening, representing an incremental advancement.
The paper tackles the problem of olfactory inertial odometry (OIO) calibration for robots navigating by scent, focusing on centimeter-level accuracy in odor source localization, and demonstrates improved performance over cold-start navigation on a real robotic arm.
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.