https://github.com/iglesias/ae-project

Project for the course EL2320 Applied Estimation at KTH - Distributed Multi-Robot Localization
https://github.com/iglesias/ae-project

Last synced: 2 months ago
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Project for the course EL2320 Applied Estimation at KTH - Distributed Multi-Robot Localization

• Host: GitHub
• URL: https://github.com/iglesias/ae-project
• Owner: iglesias
• Created: 2011-12-12T14:26:59.000Z (about 13 years ago)
• Default Branch: master
• Last Pushed: 2024-01-03T09:32:50.000Z (12 months ago)
• Last Synced: 2024-01-03T10:45:12.292Z (12 months ago)
• Language: MATLAB
• Homepage:
• Size: 1.39 MB
• Stars: 3
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README

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README

        
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