2025/06/03 by Kordel K. France, Ovidiu Daescu, France, Kordel K. +1 · 1 voice · 1 citation
Computer Science · Engineering · Neuroscience · Physics and Astronomy · #Advanced Chemical Sensor Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #Olfactory and Sensory Function Studies #Robotics (cs.RO) #Robotics and Automated Systems #Systems and Control (eess.SY) #cs.LG #cs.RO #eess.SY #electronic engineering #information engineering #physics.ins-det
paper · pdf · doi:10.48550/arxiv.2506.02373
openalex publication_date 2025/06/03 · arxiv published 2025/06/03 · arxiv updated 2025/06/03 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
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.