You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: particlefilt.m
    path of file: ./repos/Robotics-Toolbox/rvctools/release10.3.1/robot/demos
    
    the code of the file until where you have to start completion: 

% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
% 
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% 
% RTB is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU Lesser General Public License for more details.
% 
% You should have received a copy of the GNU Leser General Public License
% along with RTB.  If not, see <http://www.gnu.org/licenses/>.
%
% http://www.petercorke.com

%%begin

% Ou
    
function that maps 
% the error between expected and actual sensor observation to a weight.  The Toolbox 
% uses a 2D Gaussian for this and we need to describe it by a covariance matrix
L = diag([0.1 0.1]);

% The filter also needs a noise model to "drift" the particles at each step, that
% is the hypotheses are randomly moved to model the effect of uncertainty in the
% vehicle's pose
Q = diag([0.1, 0.1, 1*pi/180]).^2;
% the values of this matrix should be consistent with the vehicle uncertainty 
% model V given above.

% Now we create an instance of the particle filter class
pf = ParticleFilter(veh, sensor, Q, L, 1000);
% and connect it to the vehicle and the sensor and give estimates of the vehicle
% and sensor covariance (we never know this is practice).  The last argument
% is the number of particles that will be used.  Each particle represents a hypothesis
% about the vehicle's pose and a weight (or likeliness).

% Now we will run the filter for 200 time steps.  At each step the vehicle
% moves, reports its odometry and the sensor measurements and the filter updates
% its estimate of the vehicle's pose.  
%
% The green dots represent the particles.  We see that initially the pose
% hypotheses are very spread out, but soon start to cluster around the actual pose
% of the robot.  The pose is 3D (x,y, theta) so if you rotate the graph while the
% simulation is running you can see the theta dimension as well.
pf.run(200);
% all the results of the simulation are stored within the ParticleFilter object

% First let's plot the map
clf; map.plot()
% and then overlay the path actually taken by the vehicle
veh.plot_xy('b');
% and then overlay the path estimated by the filter
pf.plot_xy('r');
% which we see are pretty close once the filter gets going, the initial estimates
% (when the particles are spread widely) are not so good.

% The uncertainty of the estimate is related to the spread of the particles and
% we can plot that
figure; plot(pf.std); xlabel('time step'); ylabel('standard deviation'); legend