Localization is one of the important topics in robotics and it is essential to execute a mission. Most problems in the class of localization are due to uncertainties in the modelin...
Young-Joong Kim, Chan-Hee Won, Jung-Min Pak, Myo-T...
Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has ...
We present in this paper a GPU-accelerated particle filter based on pixel-level segmentation and matching, for real-time object tracking. The proposed method achieves real-time pe...
Particle filters provide a robust framework for nonlinear and non-Gaussian estimation problems. In this paper, we present a method to incorporate dominant modulation-domain (Ampl...
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...