  Robust environment perception is essential for decision-making on robots
operating in complex domains. Principled treatment of uncertainty sources in a
robot's observation model is necessary for accurate mapping and object
detection. This is important not only for low-level observations (e.g.,
accelerometer data), but for high-level observations such as semantic object
labels as well. This paper presents an approach for filtering sequences of
object classification probabilities using online modeling of the noise
characteristics of the classifier outputs. A hierarchical Bayesian approach is
used to model per-class noise distributions, while simultaneously allowing
sharing of high-level noise characteristics between classes. The proposed
filtering scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown
to outperform classification accuracy of existing methods. The paper also
presents real-time filtered classification hardware experiments running fully
onboard a moving quadrotor, where the proposed approach is demonstrated to work
in a challenging domain where noise-agnostic filtering fails.
