  Modern robotic platforms need a reliable localization system to operate daily
beside humans. Simple pose estimation algorithms based on filtered wheel and
inertial odometry often fail in the presence of abrupt kinematic changes and
wheel slips. Moreover, despite the recent success of visual odometry, service
and assistive robotic tasks often present challenging environmental conditions
where visual-based solutions fail due to poor lighting or repetitive feature
patterns. In this work, we propose an innovative online learning approach for
wheel odometry correction, paving the way for a robust multi-source
localization system. An efficient attention-based neural network architecture
has been studied to combine precise performances with real-time inference. The
proposed solution shows remarkable results compared to a standard neural
network and filter-based odometry correction algorithms. Nonetheless, the
online learning paradigm avoids the time-consuming data collection procedure
and can be adopted on a generic robotic platform on-the-fly.
