Monte Carlo Localization with LEGO NXT & MATLAB
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Uploaded by ricardocrl on Jun 15, 2010
This is a Monte Carlo Localization demonstration using a LEGO Mindstorms NXT Robot. It uses an IR remote control to control the odometry and the sensors are an ultrasonic and a compass sensor from HiTechnic.
The robot runs an embedded NXC program to generate the odometry motion model, based on the servos tachometers, retrieving the information to a laptop through bluetooth.
The filtering step with the sensor model is processed in MATLAB and uses RWTH toolbox to get the odometry.
The map dimension is 122 x 160cm.
The algorithm estimates x, y and theta and reaches a fairly good stability with about 150 particles.
Uploader Comments (ricardocrl)
All Comments (10)
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nerds. 1000 times preferable business than low pay scientific people.
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How did you decided the first position of the robot , from where the robot starts localization .. And what about filtering the scanned data. Did you use Kalman Filter or any other technique?
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Okey Dokey, thanks for clearing that up!
14:27
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What exactly is it supposed to do?
HedgieRobot72 1 year ago
@HedgieRobot72
The robot is trying to localize itself, based on both sensor measurements and control given from a remote control. It's always an estimate (better or worse), never the true position.
ricardocrl 1 year ago
Call me stupid but what is the robot trying to achieve?
boonce88 1 year ago
@boonce88
why would you be stupid? for asking? :P
It's trying to localize itself, based on both sensor measurements and control given from a remote control. It's always an estimate (better or worse), never the true position.
ricardocrl 1 year ago
That's very cool!
I'm just starting out a pathfinding project using A* pathfinding and a grid. I hope to be able to run it within the NXT, but I'm not sure I have enough memory for it. This is very cool though. Must have been a b**ch to tweak... :-)
atledreier 1 year ago
@atledreier
Actually it wasn't very hard to adjust to the best parameters, for instance, for the variances of the gaussians. It was harder to get the odometry model working fine. :-)
Good luck for your project! I believe it's not easy to do it all in the NXT...
ricardocrl 1 year ago