Assignment code for Georgia Tech CS 3630, 2014
Add completed RangeSensor, add screenshots
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Assignment_3/code/matlab/RangeSensor.m
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Assignment_3/code/matlab/RangeSensor.m
···
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% % A lower score means it is less likely to be selected for the next generation...
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% % The weight is never zero.
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% end
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weights = ones(length(x),1);
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for i = 1 : length(x)
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z_actual = z;
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z_pred = s.h(x(i,:)');
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z_diff = z_actual - z_pred;
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weights(i) = exp(-0.5 * z_diff ^ 2 * L);
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weights(i) = exp(-0.5 * z_diff' * L * z_diff) + weights(i);
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% Equation from:
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% Robotics, Vision & Control, Chap 6,
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% Peter Corke,
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% Springer 2011
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end
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end
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Assignment_3/code/matlab/log-1423005664.txt
Assignment_3/code/matlab/log-boxworld.txt
Assignment_3/code/matlab/log-1423005664.txt
Assignment_3/code/matlab/log-boxworld.txt
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Assignment_3/code/matlab/script01_MotionModel.m
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Assignment_3/code/matlab/script01_MotionModel.m
···
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% demo08_squareWorldMCL
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% Demo generic Monte Carlo Localization
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path(path,'threedee')
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path(path,'threedee')
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log = 'log-boxworld.txt'
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%% Create a map
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A = [
···
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%% and a robot with noisy odometry
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V=diag([0.01, 0.1*pi/180].^2)
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veh=Differential(V, 'x0', [-20, -3, 0])
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veh.add_driver(DeterministicPath('log-1423005664.txt'));
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veh.add_driver(DeterministicPath(log));
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%% and then a sensor with noisy readings
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W=0.05^2;
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sensor = RangeSensor(veh,map, W,'log-1423005664.txt')
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sensor = RangeSensor(veh,map, W,log)
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%% define two covariances for random noise Q and L (hmmm!)
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% For Q, use the uncertainly estimates from A2!
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Q = diag([0.01,0.01,0.1*pi/180].^2);
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L = diag(0.1);
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Q = 25 .* diag([0.01,0.01,0.1*pi/180].^2);
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L = 25 .* diag(0.1);
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%% Finally, construct ParticleFilter
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pf = GenericParticleFilter(veh, sensor, Q, L, 200);
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Assignment_3/code/matlab/script02_SensorModel.m
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Assignment_3/code/matlab/script02_SensorModel.m
···
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% Demo generic Monte Carlo Localization
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path(path,'threedee')
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log = 'log-boxworld.txt'
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%% Create a map
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A = [
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0, 0, 15.5, 11.5
···
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%% and a robot with noisy odometry
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V=diag([0.01, 0.1*pi/180].^2)
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veh=Differential(V, 'x0', [-20, -3, 0])
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veh.add_driver(DeterministicPath('log-1423005664.txt'));
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veh.add_driver(DeterministicPath(log));
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%% and then a sensor with noisy readings
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W=0.05^2;
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sensor = RangeSensor(veh,map, W,'log-1423005664.txt')
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sensor = RangeSensor(veh,map, W, log)
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%% define two covariances for random noise Q and L (hmmm!)
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% For Q, use the uncertainly estimates from A2!
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Q = diag([0.01,0.01,0.1*pi/180].^2);
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L = diag(0.1);
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Q = 25 .* diag([0.01,0.01,0.1*pi/180].^2);
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L = 25 .* diag(0.1);
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%% Finally, construct ParticleFilter
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pf = GenericParticleFilter(veh, sensor, Q, L, 200);
Assignment_3/images/FullFiltering.png
Assignment_3/images/FullFiltering.png
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Assignment_3/images/MotionModel.png
Assignment_3/images/MotionModel.png
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Assignment_3/images/log-checkpoint.png
Assignment_3/images/log-checkpoint.png
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Assignment_3/images/verify-checkpoint.fla
Assignment_3/images/verify-checkpoint.fla
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