Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network
This work addresses localization challenges for service and assistive robots in environments where visual odometry fails, offering an incremental improvement in multi-source localization systems.
The paper tackles the problem of unreliable wheel odometry in mobile robots due to abrupt kinematic changes and wheel slips by proposing an online learning approach for wheel odometry correction using an attention-based neural network, achieving remarkable results compared to standard methods and enabling real-time inference without time-consuming data collection.
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.