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TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Fusion Applied to Soccer Robots Sensor Fusion Applied to Soccer Robots Prepared.

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Presentation on theme: "TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Fusion Applied to Soccer Robots Sensor Fusion Applied to Soccer Robots Prepared."— Presentation transcript:

1 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Fusion Applied to Soccer Robots Sensor Fusion Applied to Soccer Robots Prepared by: Pedro Marcelino Oriented by: Prof. Pedro Lima

2 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions Topics Sensor Fusion Topics

3 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

4 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Increased interest in the developing of multi-sensor robots Limitations in the reconstruction of environments Observation errors, bad calibrations or partial and incomplete information of the world Cooperation to resolve ambiguities Robust and consistent description of the world Team with a common goal and shared knowledge, so it can take the right decisions. Motivation Sensor Fusion Motivation

5 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

6 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Complexity Observation Error Observation Disparity Multiples Points of View Sensors Caracteristics Sensor Fusion Sensors Caracteristics

7 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

8 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Multi-Sensorial System = Team of Sensors Each sensor is considerer an individual Each sensor make local decisions Each sensor implements its actions The Team coordinate the activity of its members Information exchange to resolve conflits and validation of observations Makes the Team Decision Problema a simple Estimation Problem Sensor as a Team Member Sensor Fusion Sensor as a Team Member

9 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

10 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Observation Model It is a static description of the sensor performance, realting the observation with the state of teh environment Front Camera Model Up Camera Model Sensor Models Sensor Fusion Sensor Models CLCL C

11 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Front Camera Observation Model Sensor Fusion Front Camera Observation Model

12 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Up Camera Observation Model Sensor Fusion Up Camera Observation Model

13 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 State Model Relates the observation of a sensor with a given location and its internal state Perspective change to a common frame so that the observation can be compared Sensor Models Sensor Fusion Sensor Models

14 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Each feature is represented as with a gauss distribuition Mean Variance Angle with central axis Distance to feature New variance results from the perspective transformation to a global frame State Model Sensor Fusion State Model

15 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Dependency Model Describe sthe relation between the observations and the actions of each sensor Team Utility Function Team Decision Fucntion Groups Rational Aximos Each member makes a decision that maximizes its Team Utility Function Sensor Models Sensor Fusion Sensor Models

16 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

17 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Observation Integration Sensor Fusion Observation Integration Each feature is modeled by a gauss distribution, using Bayes Law If the Mahalanobis distance is less than 1, then there is agreement and the team member will cooperate, to estimate the feature position, otherwise, there is desagreement and the team member observation will not be used

18 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Two bayes observers showing agreement Observation Integration Sensor Fusion Observation Integration

19 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Two bayes observers showing desagreemnet Observation Integration Sensor Fusion Observation Integration

20 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

21 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Ball detection in front Camera Implemented Algoritms – Ball Detection Sensor Fusion Implemented Algoritms – Ball Detection

22 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Ball detection un Up Camera Implemented Algoritms – Ball Detection Sensor Fusion Implemented Algoritms – Ball Detection

23 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Camera Models Sensor Fusion Camera Models

24 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Models Diagram Sensor Fusion Sensor Models Diagram Observation Model State Model Observation of Sensor 2 Dependency Model Team Utility Function Sensor Model Change of perspective to Global Frame Decision and Integration of Observation Structure that keeps all decisions made by the team members New Fusion Validation Variance Increase with Time Observation Model State Model Observation of Sensor 1

25 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

26 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Experimental Results Sensor Fusion Experimental Results

27 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Topics Sensor Fusion Topics Motivation Sensors Caracteristics Sensors as Members of a Team Sensor Models Observation Integration Implemented Algoritms Experimental Results Conclusions

28 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Conclusions Sensor Fusion Conclusions Real time fusion of the world information Good estimative of features localization Makes system more robust, eliminating sporadic errors Coerent World decription Use of Bayes Teorem to solve the decision problem It is a really good method to be used in modern robotics, which should be used whenever possible to determine the position and orientation of the environment features that surrond the robot

29 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Future Work Sensor Fusion Future Work To be developed during the Master Sensor Fusion of several robots Other players detection Team players detection Sensor Fusion to determine robot position

30 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Sensor Fusion Diagram Sensor Fusion Sensor Fusion Diagram Sensors Up CameraFront CameraSonarsOdometry Observation and State Model Up CameraFront CameraSonarsOdometry BlackBoard local.up.*local.front.*local.sonars.*local.odometry.* Dependency Model Local Sensor Fusion Algoritm BlackBoard global.worldmodel.* World Model Global Sensor Fusion Algoritm Dependency Model Local Sensor Fusion Algoritm of Other Robots

31 TFC Sensor Fusion– Pedro Marcelino LEIC 27 de Fevereiro de 2003 Team Members Docentes do IST: Pedro Lima (coordenação) - DEEC Luis Custódio (coordenação) - DEEC Carlos Pinto Ferreira (professor associado) - DEM Alunos de Doutoramento (EEC): Miguel Garção Alunos Finalistas (TFC): Bruno Damas - LEEC Pedro Pinheiro - LEIC Hugo Costelha - LEEC Gonçalo Neto - LEEC Cláudio Gil – LEIC Miguel Arroz – LEIC Bruno – LEIC


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