Delayed-state information filter for cooperative decentralized tracking

Authors:
J. Capitán;L. Merino;F. Caballero;A. Ollero
Affiliations:
University of Seville, Camino de los Descubrimientos s/n, Sevilla, Spain;Pablo de Olavide University, Sevilla, Spain;University of Seville, Camino de los Descubrimientos s/n, Sevilla, Spain;University of Seville, Camino de los Descubrimientos s/n, Sevilla, Spain
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 3
Cited 3

A probabilistic framework for entire WSN localization using a mobile robot

Robotics and Autonomous Systems
Decentralized sensor fusion with distributed particle filters

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Exactly Sparse Delayed-State Filters for View-Based SLAM

IEEE Transactions on Robotics

Experimental Results in Multi-UAV Coordination for Disaster Management and Civil Security Applications

Journal of Intelligent and Robotic Systems
Decentralized Delayed-State Information Filter (DDSIF): A new approach for cooperative decentralized tracking

Robotics and Autonomous Systems
Decentralized Cooperative SLAM for Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. Particularly, a Decentralized Delayed-State Extended Information Filter (DDSEIF) is described, where full state trajectories are considered to fuse the information. This permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications. The sparseness of the information matrix maintains the communications overhead at a reasonable level. The method is applied to cooperative tracking and some results in disaster management scenarios are shown. In this kind of scenarios the target might move in both open field and indoor areas, so fusion of data provided by heterogeneous sensors is beneficial.