# Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent

arXiv: https://arxiv.org/abs/2506.02373 · HTML: https://mlsanity.com/paper/2506.02373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments](https://mlsanity.com/paper/2107.05490/) (2021)
- [Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks](https://mlsanity.com/paper/2108.08793/) (2021)
- [Exploiting plume structure to decode gas source distance using metal-oxide gas sensors](https://mlsanity.com/paper/1602.01815/) (2016)
- [Does a robot path have clearance c?](https://mlsanity.com/paper/1807.09392/) (2018)

## Abstract (arXiv)

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.