Revise comments

Author: sunglok

apply_noise_gauss.m

@@ -13,7 +13,7 @@
 %       as isotropic, STDEV * eye(M,M). Similarly, 1xM standard deviation, STDEV,
 %       is interpreted as diagonal, diag(STDEV).
 %
-%   Examples:
+%   Example:
 %       in = zeros(100, 2);
 %       out = apply_noise_gauss(in, 3)
 %       out = apply_noise_gauss(in, [1, 2])

error_orientation.m

@@ -13,7 +13,7 @@
 %       of an object is described by x-axis in its local coordinate, error between
 %       two orientation comes from angular difference of two x axes.
 %
-%   Examples:
+%   Example:
 %       rad = error_orientation([0, 0, 0], [0, 0, pi])
 
 v_p = tran_rad2rot(p) * [1; 0; 0];

error_position.m

@@ -8,7 +8,7 @@
 %
 %   Note: Two vectors, P and Q, should be same size.
 %
-%   Examples:
+%   Example:
 %       p = [82; 3; 29];
 %       q = [84; 10; 18];
 %       d = error_position(p, q)

localize2d_betke97.m

@@ -15,12 +15,12 @@
 %       element of POSE. Since this algorithm estimates 2D position and orientation,
 %       the expected VALID is [true, true, false, false, false, true].
 %
-%   References:
+%   Reference:
 %       [1] M. Betke and L. Gurvits, Mobile Robot Localization using Landmarks,
 %           IEEE Transactions on Robotics and Automation, Vol. 13, No. 2, 1997
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=563647
 %
-%   Examples:
+%   Example:
 %       N = 3;
 %       map = [10 * rand(N,2), zeros(N,4)]; % Random 2D landmark map
 %       data = [2 * pi * rand(N,1) - pi, zeros(N,1)]; % Random measurement

localize2d_sayed05.m

@@ -14,12 +14,12 @@
 %       element of POSE. Since this algorithm estimates 2D position, the expected
 %       VALID is [true, true, false, false, false, false].
 %
-%   References:
+%   Reference:
 %       [1] A. H. Sayed et al., Network-based Wireless Location,
 %           IEEE Signal Processing Magazine, Vol. 24, No. 4, 2005
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1458275
 %
-%   Examples:
+%   Example:
 %       N = 3;
 %       map = [10 * rand(N,2), zeros(N,4)]; % Random 2D landmark map
 %       data = 10 * rand(N,1); % Random measurement

localize2d_se05.m

@@ -18,12 +18,12 @@
 %   Note: This implementation is extended from Se et al. [1], so it can take into account
 %       more than two measurements in lease-squares sense.
 %
-%   References:
+%   Reference:
 %       [1] S. Se et al., Vision-Based Global Localization and Mapping for Mobile Robots,
 %           IEEE Transactions on Robotics, Vol. 21, No. 3, 2005
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1435480
 %
-%   Examples:
+%   Example:
 %       N = 2;
 %       map = [10 * rand(N,2), zeros(N,4)]; % Random 2D landmark map
 %       data = [10 * rand(N,2), zeros(N,1)]; % Random measurement

localize2d_shimshoni02_algebraic.m

@@ -18,12 +18,12 @@
 %   Note: This implementation is based on Shimshoni's basic algebraic approach [1]
 %       without the proposed improvement.
 %
-%   References:
+%   Reference:
 %       [1] I. Shimshoni, On Mobile Robot Localization from Landmark Bearings,
 %           IEEE Transactions on Robotics and Automation, Vol. 18, No. 6, 2002
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1159015
 %
-%   Examples:
+%   Example:
 %       N = 3;
 %       map = [10 * rand(N,2), zeros(N,4)]; % Random 2D landmark map
 %       data = [2 * pi * rand(N,1) - pi, zeros(N,1)]; % Random measurement

localize2d_shimshoni02_improved.m

@@ -18,12 +18,12 @@
 %   Note: This implementation is based on Shimshoni's algebraic approach with the first
 %       and second improvements, bounded variance and scale data.
 %
-%   References:
+%   Reference:
 %       [1] I. Shimshoni, On Mobile Robot Localization from Landmark Bearings,
 %           IEEE Transactions on Robotics and Automation, Vol. 18, No. 6, 2002
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1159015
 %
-%   Examples:
+%   Example:
 %       N = 3;
 %       map = [10 * rand(N,2), zeros(N,4)]; % Random 2D landmark map
 %       data = [2 * pi * rand(N,1) - pi, zeros(N,1)]; % Random measurement

localize3d_sayed05.m

@@ -14,12 +14,12 @@
 %       element of POSE. Since this algorithm estimates 3D position, the expected
 %       VALID is [true, true, true, false, false, false].
 %
-%   References:
+%   Reference:
 %       [1] A. H. Sayed et al., Network-based Wireless Location,
 %           IEEE Signal Processing Magazine, Vol. 24, No. 4, 2005
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1458275
 %
-%   Examples:
+%   Example:
 %       N = 4;
 %       map = [10 * rand(N,3), zeros(N,3)]; % Random 2D landmark map
 %       data = 10 * rand(N,1); % Random measurement

localize3d_thomas05.m

@@ -19,15 +19,15 @@
 %       exactly three, this algorithm may return two sets of POSE and VALID due to ambiguity.
 %       If N is more than three, this algorithm uses 4-th measurement to resolve ambiguity.
 %
-%   Note: This implementation is slightly modified from Thomas's original code once available at
-%       his homepage, http://www-iri.upc.es/people/~thomas.
+%   Note: This implementation is slightly modified from Thomas's original code available at
+%       his homepage, http://www.iri.upc.edu/people/thomas/.
 %
-%   References:
+%   Reference:
 %       [1] F. Thomas and L. Ros, Revisiting Trilateration for Robot Localization,
 %           IEEE Transactions on Robotics, Vol. 21, No. 1, 2005
 %           URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1391018
 %
-%   Examples:
+%   Example:
 %       N = 4;
 %       map = [10 * rand(N,3), zeros(N,3)]; % Random 2D landmark map
 %       data = 10 * rand(N,1); % Random measurement

observe_bearing.m

@@ -5,7 +5,7 @@
 %       (matrix) MAP         : A landmark map (Nx6 matrix)
 %       (matrix) POSE        : Pose of the target object (1x6 matrix)
 %       (scalar) VISIBLE_RATE: Visible probability of landmarks (default: 1)
-%       (matrix) OBS_DATA    : The measured displacement from POSE to landmarks (Mx2 matrix)
+%       (matrix) OBS_DATA    : The measured bearing from POSE to landmarks (Mx2 matrix)
 %       (matrix) OBS_MAP     : The landmark map of measured landmarks (Mx6 matrix)
 %
 %   Note: Pose of an object, POSE, is represented by 1x6 vector whose first three

observe_displacement.m

@@ -24,7 +24,7 @@
 %   Note: The measured displacement, OBS_DATA, is represented by Mx3 matrix whose i-th row
 %       is relative position of i-th landmark in OBS_MAP with respect to the given pose, POSE.
 %
-%   Examples:
+%   Example:
 %       map  = [ 0, 0, 5, 0, 0, 0; ...
 %                5, 0, 5, 0, 0, 0; ...
 %                5, 5, 5, 0, 0, 0 ];

observe_distance.m

@@ -21,7 +21,7 @@
 %       If there is no visible landmark, OBS_DATA and OBS_MAP will be an empty matrix.
 %       Please use the command, ISEMPTY, to identify an empty matrix.
 %
-%   Examples:
+%   Example:
 %       map  = [ 0, 0, 5, 0, 0, 0; ...
 %                5, 0, 5, 0, 0, 0; ...
 %                5, 5, 5, 0, 0, 0 ];

observe_pose.m

@@ -24,7 +24,7 @@
 %   Note: The measured relative pose, OBS_DATA, is represented by Mx6 matrix whose
 %       format is exactly same with POSE and MAP.
 %
-%   Examples:
+%   Example:
 %       map  = [ 0, 0, 5, 0, 0, 0; ...
 %                5, 0, 5, 0, 0, 0; ...
 %                5, 5, 5, 0, 0, 0 ];

run_eval_roh.m

@@ -29,7 +29,7 @@
     4.5, 3.0, 0, 0, 0, pi/2; ...
     4.5, 4.5, 0, 0, 0, pi/2; ...
 ];
-config.algorithm = ...  % Description of localization algorithms
+config.algorithm = ... % Description of localization algorithms
 {                                                                                                       ...
   % #, Dim, Name,         Local. Function,      Observation Function,     Min. N, Valid,    Line Sytle; ...
     1,  2,  'Betke97',    @localize2d_betke97,  @observe_bearing,              3, [1 1 0 0 0 1], 'gd-'; ...
@@ -42,7 +42,7 @@
                  'Computing Time [msec]', 'Number of Failures'}; % Name of evaluation criteria
 criteria.repr = {@median, @median, @median, @sum}; % Functions for calculating representive values
                                                    %  (e.g. mean, median, std, and sum)
-criteria.format = {'%.6f', '%.3f', '%.6f', '%d'}; % Format for printing text
+criteria.format = {'%.6f', '%.3f', '%.6f', '%d'};  % Format for printing text
 
 % Perform experiments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 config.algoDims = 2;
@@ -87,4 +87,4 @@
 % 4. Save experimental results
 save(config.matFile, 'config', 'criteria', 'record');
 
-% To visualize the result, please use the script, 'run_anal_record', with 'target.ex = 1' and 'target.v = 1'.
+% To visualize the result, please use the script, 'run_draw_distribution', with 'target.ex = 1' and 'target.v = 1'.

save_figure.m

@@ -9,7 +9,7 @@ function save_figure(h, filename)
 %       file format. (e.g. 'output.jpg') Please refer to the command, SAVEAS, to
 %       check the available file formats.
 %
-%   Examples:
+%   Example:
 %       save_figure(gcf, 'output.pdf')
 %       save_figure(gcf, 'output.png');
 %

test_is_near.m

@@ -10,7 +10,7 @@
 %
 %   Note: Two values, A and B, should be same size.
 %
-%   Examples:
+%   Example:
 %       t = test_is_near(4.17, 4.19, 0.1)
 %       t = test_is_near(pi, pi + eps)
 %       t = test_is_near([3, 29], [3 + eps, 29 - eps])

test_is_true.m

@@ -6,7 +6,7 @@
 %       (scalar) VERBOSE: A flag to print its result (default: true)
 %       (scalar) RESULT : The test result
 %
-%   Examples:
+%   Example:
 %       t = test_is_true(82 < 84, false)
 %       t = test_is_true([10, 18] == [10, 18])
 %       t = test_is_true(isequal([1, 2; 3, 4], [5, 6]))

tran_deg2rad.m

@@ -5,7 +5,7 @@
 %       (matrix) DEGREE: The given angle [deg]
 %       (matrix) RADIAN: The transformed angle [rad]
 %
-%   Examples:
+%   Example:
 %       t = tran_deg2rad(30)
 %       t = tran_deg2rad(60)
 %

tran_rad2deg.m

@@ -5,7 +5,7 @@
 %       (matrix) RADIAN: The given angle [rad]
 %       (matrix) DEGREE: The transformed angle [deg]
 %
-%   Examples:
+%   Example:
 %       t = tran_rad2deg(pi / 6)
 %       t = tran_rad2deg(pi / 3)
 %

tran_rad2rot.m

@@ -8,12 +8,12 @@
 %   Note: Orientation, RADIAN, is represented by 1x3 matrix whose elements are
 %       rotation angle with respect to x, y, and z axes, respectively.
 %
-%   References:
+%   Reference:
 %       [1] S.M. LaValle, Planning Algorithm, Cambridge, 2006,
 %           URL: http://planning.cs.uiuc.edu/node102.html
 %           URL: http://planning.cs.uiuc.edu/node103.html
 %
-%   Examples:
+%   Example:
 %       R = tran_rad2rot([pi/3, pi/4, pi/6])
 %
 %   See also tran_rot2rad.

tran_rot2rad.m

@@ -10,12 +10,12 @@
 %
 %   Note: R(1,1) and R(3,3) should not be zero due to singularity.
 %
-%   References:
+%   Reference:
 %       [1] S.M. LaValle, Planning Algorithm, Cambridge, 2006,
 %           URL: http://planning.cs.uiuc.edu/node102.html
 %           URL: http://planning.cs.uiuc.edu/node103.html
 %
-%   Examples:
+%   Example:
 %       R = tran_rad2rot([pi/6, pi/6, pi/6]);
 %       angle = tran_rot2rad(R)
 %

trim_rad.m

@@ -5,7 +5,7 @@
 %       (matrix) IN : The given angle [rad]
 %       (matrix) OUT: The trimmed angle [rad]
 %
-%   Examples:
+%   Example:
 %       t = trim_rad(2 * pi)
 %       t = trim_rad(0 : pi/6 : 4*pi)