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.

翻译：现代机器人平台需要可靠的定位系统以在人类日常环境中运行。基于滤波器和惯性里程计的简单位姿估计算法在遇到突运动学变化和车轮打滑时常常失效。此外，尽管视觉里程计近期取得了成功，服务与辅助机器人任务常面临光照不足或重复特征模式等挑战性环境条件，导致基于视觉的解决方案失效。本文提出一种创新的在线学习方法用于轮式里程计校正，为构建鲁棒的多源定位系统铺平道路。我们研究了一种高效的基于注意力神经网络的架构，以兼顾精确性能与实时推理。与标准神经网络及基于滤波器的里程计校正算法相比，所提方案展现出显著优势。同时，该在线学习范式避免了耗时的数据采集过程，并可即时应用于通用机器人平台。