Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent

--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. From the first life forms to complex mammals, the ability to navigate using scent has been a cornerstone of survival. Animals like ants, hounds, and rodents demonstrate remarkable proficiency in following odour plumes and pheromone trails to locate food, mates, or shelter. These feats are achieved through a sophisticated interplay between acute scent receptors and motion. However, the physical behavior of odour plumes--constantly shifting with wind, influenced by temperature and humidity, and weakening over time--presents a formidable challenge. When the odour source is out of sight, organisms rely entirely on olfactory cues, transforming the task into a complex control problem that demands robust uncertainty management.