Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
This work addresses the problem of enabling robots to navigate by scent, which is incremental as it adapts existing SLAM and VIO principles to the olfaction domain.
The paper tackles the challenge of robotic navigation using artificial smell by introducing olfactory inertial odometry (OIO), a framework that combines inertial kinematics and fast-sampling olfaction sensors, analogous to visual inertial odometry, and demonstrates it on a real 5-DoF robot arm in odour-tracking scenarios for applications like agriculture and food quality control, establishing a baseline framework for future research.
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.