# Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation

arXiv: https://arxiv.org/abs/2506.04539 · HTML: https://mlsanity.com/paper/2506.04539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (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)

## Abstract (arXiv)

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.