  Olfaction sensing in autonomous robotics faces challenges in dynamic
operations, energy efficiency, and edge processing. It necessitates a machine
learning algorithm capable of managing real-world odor interference, ensuring
resource efficiency for mobile robotics, and accurately estimating gas features
for critical tasks such as odor mapping, localization, and alarm generation.
This paper introduces a hybrid approach that exploits neuromorphic computing in
combination with probabilistic inference to address these demanding
requirements. Our approach implements a combination of a convolutional spiking
neural network for feature extraction and a Bayesian spiking neural network for
odor detection and identification. The developed algorithm is rigorously tested
on a dataset for sensor drift compensation for robustness evaluation.
Additionally, for efficiency evaluation, we compare the energy consumption of
our model with a non-spiking machine learning algorithm under identical dataset
and operating conditions. Our approach demonstrates superior efficiency
alongside comparable accuracy outcomes.
