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Project for the course EL2320 Applied Estimation at KTH - Distributed Multi-Robot Localization
This project forked from iglesias/ae-project
Project for the course EL2320 Applied Estimation at KTH - Distributed Multi-Robot Localization
About the notation: - Q is the covariance for the noise in the motion model (like they use it in the paper, contrary to the book). - Rfeat is the covariance for the noise in the measurement model of the features (when a robot measures a feature, as in lab1 and lab2). - Rrobo is the covariance for the noise in the measurement model of the robots (when a robot sees another robot). Thoughts for the report: - The update steps of the Kalman filter have been separated in observed and observed (the robot saw another robot or was seen by another robot) so the simulation represents better a real decentralized system. - In order to handle the cross-correlation terms we have made the decision that each of the robots takes care of one complete row of the centralized system matrix. This means that the robot 1 for example would store information about P11, P12 and P13. Robot 2 and 3 would store P21 and P23, respectively. This might be a bit redundant since P12 = P21' and P13 = P31' but it is a somewhat general representation. TODO - The non-initial update step for the robots that have not participated in the measurement. - Make in separate functions the computation of H_tilde, R_tilde
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