Abstract
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.
Abstract (translated)
现代机器人平台需要可靠的定位系统,在日常生活中与人类协同操作。基于过滤轮和惯性计测距的简单姿态估计算法,在存在突然的机械运动变化和轮子滑动的情况下常常失败。此外,尽管视觉计测距最近取得了成功,服务和支持机器人任务常常面临着挑战性的环境条件,由于照明不足或重复特征模式等原因,视觉解决方案无法适用。在本工作中,我们提出了一种创新的在线学习方法,用于修正轮子计测距,为可靠的多来源定位系统铺平了道路。我们研究了高效的注意神经网络架构,使其能够结合精确性能和实时推理。提出的解决方案相对于标准神经网络和基于滤波的计测距修正算法,表现出卓越的结果。然而,在线学习范式避免了繁琐的数据采集程序,可以应用于通用的机器人平台实时采用。
URL
https://arxiv.org/abs/2303.11725
