v01

thirdparty/opengv/matlab/benchmark_relative_pose_noncentral.m

@@ -0,0 +1,114 @@
+%% Reset everything
+
+clear all;
+clc;
+close all;
+addpath('helpers');
+
+%% Configure the benchmark
+
+% noncentral case
+cam_number = 4;
+% Getting 10 points, and testing all algorithms with the respective number of points
+pt_number = 50;
+% noise test, so no outliers
+outlier_fraction = 0.0;
+% repeat 5000 tests per noise level
+iterations = 5000;
+
+% The algorithms we want to test
+algorithms = { 'seventeenpt'; 'rel_nonlin_noncentral'; 'rel_nonlin_noncentral' };
+% This defines the number of points used for every algorithm
+indices = { [1:1:17]; [1:1:17]; [1:1:50] };
+% The name of the algorithms in the final plots
+names = { '17pt'; 'nonlin. opt. (17pts)'; 'nonlin. opt. (50pts)' };
+
+% The maximum noise to analyze
+max_noise = 5.0;
+% The step in between different noise levels
+noise_step = 0.1;
+
+%% Run the benchmark
+
+%prepare the overall result arrays
+number_noise_levels = max_noise / noise_step + 1;
+num_algorithms = size(algorithms,1);
+mean_rotation_errors = zeros(num_algorithms,number_noise_levels);
+median_rotation_errors = zeros(num_algorithms,number_noise_levels);
+mean_position_errors = zeros(num_algorithms,number_noise_levels);
+median_position_errors = zeros(num_algorithms,number_noise_levels);
+noise_levels = zeros(1,number_noise_levels);
+
+%Run the experiment
+for n=1:number_noise_levels
+
+    noise = (n - 1) * noise_step;
+    noise_levels(1,n) = noise;
+    display(['Analyzing noise level: ' num2str(noise)])
+    
+    rotation_errors = zeros(num_algorithms,iterations);
+    position_errors = zeros(num_algorithms,iterations);
+    
+    counter = 0;
+    
+    for i=1:iterations
+        
+        % generate experiment        
+        [v1,v2,t,R] = create2D2DExperiment(pt_number,cam_number,noise,outlier_fraction);
+        [t_perturbed,R_perturbed] = perturb(t,R,0.01);
+        T_perturbed = [R_perturbed,t_perturbed];
+        T_gt = [R,t];
+        
+        for a=1:num_algorithms
+            Out = opengv(algorithms{a},indices{a},v1,v2,T_perturbed);
+            [position_error, rotation_error] = evaluateTransformationError( T_gt, Out );
+            position_errors(a,i) = position_error;
+            rotation_errors(a,i) = rotation_error;
+        end
+        
+        counter = counter + 1;
+        if counter == 100
+            counter = 0;
+            display(['Iteration ' num2str(i) ' of ' num2str(iterations) '(noise level ' num2str(noise) ')']);
+        end        
+    end
+
+    %Now compute the mean and median value of the error for each algorithm
+    for a=1:num_algorithms
+        mean_rotation_errors(a,n) = mean(rotation_errors(a,:));
+        median_rotation_errors(a,n) = median(rotation_errors(a,:));
+        mean_position_errors(a,n) = mean(position_errors(a,:));
+        median_position_errors(a,n) = median(position_errors(a,:));
+    end
+    
+end
+
+%% Plot the results
+
+figure(1)
+plot(noise_levels,mean_rotation_errors,'LineWidth',2)
+legend(names,'Location','NorthWest')
+xlabel('noise level [pix]')
+ylabel('mean rot. error [rad]')
+grid on
+
+figure(2)
+plot(noise_levels,median_rotation_errors,'LineWidth',2)
+legend(names,'Location','NorthWest')
+xlabel('noise level [pix]')
+ylabel('median rot. error [rad]')
+grid on
+
+figure(3)
+plot(noise_levels,mean_position_errors,'LineWidth',2)
+legend(names,'Location','NorthWest')
+xlabel('noise level [pix]')
+ylabel('mean pos. error [m]')
+grid on
+
+figure(4)
+plot(noise_levels,median_position_errors,'LineWidth',2)
+legend(names,'Location','NorthWest')
+xlabel('noise level [pix]')
+ylabel('median pos. error [m]')
+grid on