SUMMARYSelf-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

中文翻译：

---

基于双通道无味粒子滤波的高动态环境自定位

摘要对于计算资源有限的类人机器人来说，高度动态环境中的自我定位仍然是一个具有挑战性的问题。在本文中，我们提出了一种基于双通道无味粒子滤波器（DC-UPF）的定位方法来解决这个问题。这种方法的一个关键新颖之处在于它在粒子滤波器的测量更新过程中采用了双通道开关机制，解决了运动中的稀疏视觉特征，并利用了来自相机、步行里程计和惯性测量单元的数据。NAO 机器人的大量实验表明，DC-UPF 在准确性方面优于 UPF 和蒙特卡罗定位。