no-bull.sh / CS-3630
Assignment code for Georgia Tech CS 3630, 2014
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Assignment 4/code/log-out.txt
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+2 -2
Assignment 4/code/matlab/DeterministicPath.m Assignment 4/full submission/code/matlab/DeterministicPath.m
··· 48 48 L = 5.75; 49 49 50 50 % calculate speed 51 - u(1) = 4.3 * r.*((ScaledUL+ScaledUR)./2); 52 - u(2) = 5.35 * (r./L).*(ScaledUR-ScaledUL); 51 + u(1) = 4.375 * r.*((ScaledUL+ScaledUR)./2); 52 + u(2) = 5.25 * (r./L).*(ScaledUR-ScaledUL); 53 53 measurement = driver.log{3}(driver.counter); 54 54 55 55 if measurement{1} == '0'
+2
Assignment 4/code/matlab/GenericEKF.m Assignment 4/full submission/code/matlab/GenericEKF.m
··· 560 560 P = ekf.P_est(n:n+1,n:n+1); 561 561 % TODO reinstate the interval feature 562 562 %plot_ellipse(xf, P, interval, 0, [], varargin{:}); 563 + hold on 563 564 plot_ellipse(covar^2*P, xf, varargin{:}); 564 565 plot(xf(1), xf(2), '+') 566 + hold off 565 567 xy = [xy xf]; 566 568 end 567 569
Assignment 4/code/matlab/GenericRangeBearingSensor.m Assignment 4/full submission/code/matlab/GenericRangeBearingSensor.m
+2 -2
Assignment 4/code/matlab/GenericVehicle.m Assignment 4/full submission/code/matlab/GenericVehicle.m
··· 449 449 % Notes:: 450 450 % - The path is extracted from the x_hist property. 451 451 452 - xyt = veh.x_hist 452 + xyt = veh.x_hist; 453 453 if nargout == 0 454 - plot(xyt(:,1), xyt(:,2), varargin{:}); 454 + plot(xyt(:,1), xyt(:,2), varargin{:}) 455 455 else 456 456 out = xyt; 457 457 end
Assignment 4/code/matlab/LandmarkMap.m Assignment 4/full submission/code/matlab/LandmarkMap.m
+3 -1
Assignment 4/code/matlab/SLAM.m Assignment 4/full submission/code/matlab/SLAM.m
··· 44 44 % Now we will run the filter for 1000 time steps. At each step the vehicle 45 45 % moves, reports its odometry and the sensor measurements and the filter updates 46 46 % its estimate of the vehicle's pose 47 - ekf.run(596); %596 non-closure %870 closure 47 + ekf.run(569); %596 non-closure %870 closure 48 48 % all the results of the simulation are stored within the EKF object 49 49 50 50 % First let's plot the map ··· 56 56 % which we see are pretty close 57 57 58 58 % Now let's plot the error in estimating the pose 59 + figure 59 60 ekf.plot_error() 60 61 % and this is overlaid with the estimated covariance of the error. 61 62 62 63 % Remember that the SLAM filter has not only estimated the robot's pose, it has 63 64 % simultaneously estimated the positions of the landmarks as well. How well did it 64 65 % do at that task? We will show the landmarks in the map again 66 + figure 65 67 map.plot(); 66 68 % and this time overlay the estimated landmark (with a +) and the 3sigma 67 69 % uncertainty bounds as green ellipses
Assignment 4/code/matlab/aprilTag/Makefile Assignment 4/full submission/code/matlab/aprilTag/Makefile
Assignment 4/code/matlab/aprilTag/apriltag.c Assignment 4/full submission/code/matlab/aprilTag/apriltag.c
Assignment 4/code/matlab/aprilTag/apriltag.h Assignment 4/full submission/code/matlab/aprilTag/apriltag.h
Assignment 4/code/matlab/aprilTag/apriltag.o Assignment 4/full submission/code/matlab/aprilTag/apriltag.o
Assignment 4/code/matlab/aprilTag/apriltag_demo Assignment 4/full submission/code/matlab/aprilTag/apriltag_demo
Assignment 4/code/matlab/aprilTag/apriltag_demo.c Assignment 4/full submission/code/matlab/aprilTag/apriltag_demo.c
Assignment 4/code/matlab/aprilTag/apriltag_demo.o Assignment 4/full submission/code/matlab/aprilTag/apriltag_demo.o
Assignment 4/code/matlab/aprilTag/apriltag_quad_thresh.c Assignment 4/full submission/code/matlab/aprilTag/apriltag_quad_thresh.c
Assignment 4/code/matlab/aprilTag/apriltag_quad_thresh.o Assignment 4/full submission/code/matlab/aprilTag/apriltag_quad_thresh.o
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