基于注意力神经网络的移动机器人轮式里程计在线校正学习
Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network
AI总结：
本文提出一种基于注意力神经网络的轮式里程计在线校正方法，以应对车轮打滑和运动突变，实现实时推理，性能优于标准神经网络和滤波校正算法，并支持在通用机器人平台上即时部署。
摘要：
现代机器人平台需要可靠的定位系统，以便在人类身边进行日常操作。基于滤波的轮式和惯性里程计的简单位姿估计算法在出现突然的运动学变化和车轮打滑时往往会失效。此外，尽管视觉里程计近期取得了成功，但服务和辅助机器人任务经常面临具有挑战性的环境条件，在这些条件下，基于视觉的解决方案会因光照不足或重复的特征模式而失败。在这项工作中，我们提出了一种创新的轮式里程计在线校正学习方法，为鲁棒的多源定位系统铺平了道路。我们研究了一种高效的基于注意力的神经网络架构，以将精确性能与实时推理相结合。与标准神经网络和基于滤波的里程计校正算法相比，所提出的解决方案显示出显著的结果。此外，在线学习范式避免了耗时的数据收集过程，并且可以在通用机器人平台上即时采用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
