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87 lines
3.3 KiB
87 lines
3.3 KiB
% Copyright (C) 1993-2013, by Peter I. Corke
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%
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% This file is part of The Robotics Toolbox for MATLAB (RTB).
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%
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% RTB is free software: you can redistribute it and/or modify
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% it under the terms of the GNU Lesser General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% RTB is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU Lesser General Public License for more details.
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%
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% You should have received a copy of the GNU Leser General Public License
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% along with RTB. If not, see <http://www.gnu.org/licenses/>.
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%
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% http://www.petercorke.com
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%%begin
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% Our SLAM system requires a number of components:
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% * a vehicle
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% * a map that defines the positions of some known landmarks in the world
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% * a sensor, a range-bearing sensor in this case
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% * a SLAM filter
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% Creating the vehicle. First we define the covariance of the vehicles's odometry
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% which reports distance travelled and change in heading angle
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V = diag([0.005, 0.5*pi/180].^2);
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% then use this to create an instance of a Vehicle class
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veh = Vehicle(V);
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% and then add a "driver" to move it between random waypoints in a square
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% region with dimensions from -10 to +10
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veh.add_driver( RandomPath(10) );
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% Creating the map. The map covers a square region with dimensions from
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% -10 to +10 and contains 20 randomly placed landmarks
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map = Map(20, 10);
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% Creating the sensor. We firstly define the covariance of the sensor measurements
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% which report distance and bearing angle
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W = diag([0.1, 1*pi/180].^2);
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% and then use this to create an instance of the Sensor class.
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sensor = RangeBearingSensor(veh, map, W, 'animate');
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% Note that the sensor is mounted on the moving robot and observes the features
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% in the world so it is connected to the already created Vehicle and Map objects.
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% Create the filter. First we need to determine the initial covariance of the
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% vehicle, this is our uncertainty about its pose (x, y, theta)
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P0 = diag([0.005, 0.005, 0.001].^2);
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% Now we create an instance of the EKF filter class
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ekf = EKF(veh, V, P0, sensor, W, []);
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% and connect it to the vehicle and the sensor and give estimates of the vehicle
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% and sensor covariance (we never know this is practice).
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% Now we will run the filter for 1000 time steps. At each step the vehicle
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% moves, reports its odometry and the sensor measurements and the filter updates
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% its estimate of the vehicle's pose
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ekf.run(1000);
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% all the results of the simulation are stored within the EKF object
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% First let's plot the map
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clf; map.plot()
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% and then overlay the path actually taken by the vehicle
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veh.plot_xy('b');
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% and then overlay the path estimated by the filter
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ekf.plot_xy('r');
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% which we see are pretty close
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% Now let's plot the error in estimating the pose
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ekf.plot_error()
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% and this is overlaid with the estimated covariance of the error.
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% Remember that the SLAM filter has not only estimated the robot's pose, it has
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% simultaneously estimated the positions of the landmarks as well. How well did it
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% do at that task? We will show the landmarks in the map again
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map.plot();
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% and this time overlay the estimated landmark (with a +) and the 3sigma
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% uncertainty bounds as green ellipses
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ekf.plot_map(3,'g');
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