scholarly journals Position and Velocity Tracking in Cellular Networks Using the Kalman Filter

10.5772/6808 ◽  
2009 ◽  
Author(s):  
Mohammed Olama ◽  
Seddik Djouadi ◽  
Charalambos Charalambous ◽  
Ioannis Papageorgiou ◽  
Teja Kuruganti
Keyword(s):  
Kalman Filter ◽  
Cellular Networks ◽  
Velocity Tracking

scholarly journals MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking

2021 ◽  
Vol 8 ◽  
Author(s):  
Nicola A. Piga ◽  
Fabrizio Bottarel ◽  
Claudio Fantacci ◽  
Giulia Vezzani ◽  
Ugo Pattacini ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Ground Truth ◽  
Depth Information ◽  
Loop Control ◽  
Training Time ◽  
Pose Tracking ◽  
Supplementary Material ◽  
Velocity Tracking

Tracking the 6D pose and velocity of objects represents a fundamental requirement for modern robotics manipulation tasks. This paper proposes a 6D object pose tracking algorithm, called MaskUKF, that combines deep object segmentation networks and depth information with a serial Unscented Kalman Filter to track the pose and the velocity of an object in real-time. MaskUKF achieves and in most cases surpasses state-of-the-art performance on the YCB-Video pose estimation benchmark without the need for expensive ground truth pose annotations at training time. Closed loop control experiments on the iCub humanoid platform in simulation show that joint pose and velocity tracking helps achieving higher precision and reliability than with one-shot deep pose estimation networks. A video of the experiments is available as Supplementary Material.

Stochastic Geometry Analysis of Cellular Networks

2018 ◽  
Author(s):  
Bartłomiej Błaszczyszyn ◽  
Martin Haenggi ◽  
Paul Keeler ◽  
Sayandev Mukherjee
Keyword(s):  
Cellular Networks ◽  
Stochastic Geometry ◽  
Geometry Analysis
Sign in / Sign up

Export Citation Format

Share Document