Toward a fully decentralized architecture for multi-sensor data fusion

A fully decentralized architecture is presented for data fusion problems. This architecture takes the form of a network of sensor nodes, each with its own processing facility, which together do not require any central processor or any central communication facility. In this architecture, computation is performed locally and communication occurs between any two nodes. Such an architecture has many desirable properties, including robustness to sensors failure and flexibility to the addition or loss of one or more sensors. This architecture is appropriate for the class of extended Kalman filter (EKF)-based geometric data fusion problems. The starting point for this architecture is an algorithm which allows the complete decentralization of the multisensor EKF equations among a number of sensing nodes. This algorithm is described, and it is shown how it can be applied to a number of different data-fusion problems. An application of this algorithm to the problem of multicamera, real-time tracking of objects and people moving through a room is described.<<ETX>>

[1]  Hugh F. Durrant-Whyte,et al.  Uncertain geometry in robotics , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[2]  Nancy E. Orlando An Intelligent Robotics Control Scheme , 1984, 1984 American Control Conference.

[3]  Anita M. Flynn,et al.  Combining Sonar and Infrared Sensors for Mobile Robot Navigation , 1988, Int. J. Robotics Res..

[4]  Jake K. Aggarwal,et al.  Integrated Analysis of Thermal and Visual Images for Scene Interpretation , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Olivier D. Faugeras,et al.  Building, Registrating, and Fusing Noisy Visual Maps , 1988, Int. J. Robotics Res..

[6]  Olivier D. Faugeras,et al.  Building visual maps by combining noisy stereo measurements , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[7]  N. Nandhakumar,et al.  Integrating Information From Thermal And Visual Images For Scene Analysis , 1986, Other Conferences.

[8]  Pierrick Grandjean,et al.  3-D modeling of indoor scenes by fusion of noisy range and stereo data , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[9]  Steven A. Shafer,et al.  An architecture for sensor fusion in a mobile robot , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[10]  James L. Crowley World modeling and position estimation for a mobile robot using ultrasonic ranging , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[11]  Amar Mitiche,et al.  Multisensor Knowledge Systems , 1988, Int. J. Robotics Res..

[12]  Jean-Paul Laumond,et al.  Position referencing and consistent world modeling for mobile robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[13]  Hugh Durrant-Whyte,et al.  Integration, coordination, and control of multi-sensor robot systems , 1987 .

[14]  Y. Bar-Shalom Tracking and data association , 1988 .

[15]  John Porrill,et al.  Optimal combination of multiple sensors including stereo vision , 1987, Image Vis. Comput..

[16]  Chee-Yee Chong,et al.  Joint Probabilistic Data Association in Distributed Sensor Networks , 1985, 1985 American Control Conference.

[17]  Bir Bhanu,et al.  The specification of distributed sensing and control , 1985, J. Field Robotics.

[18]  David B. Cooper,et al.  On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Gregory D. Hager,et al.  Active reduction of uncertainty in multisensor systems , 1988 .

[20]  Sumit Roy,et al.  Decentralized structures for parallel Kalman filtering , 1988 .