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KaessNewCorners_v03.m
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KaessNewCorners_v03.m
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%{
* Copyright (C) 2013-2020, The Regents of The University of Michigan.
* All rights reserved.
* This software was developed in the Biped Lab (https://www.biped.solutions/)
* under the direction of Jessy Grizzle, grizzle@umich.edu. This software may
* be available under alternative licensing terms; contact the address above.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Regents of The University of Michigan.
*
* AUTHOR: Bruce JK Huang (bjhuang[at]umich.edu)
* WEBSITE: https://www.brucerobot.com/
%}
function bag_data = KaessNewCorners_v03(opts, bag_data, tag_num)
ransac_threshold = 0.02;
d = bag_data.lidar_target(tag_num).tag_size * sqrt(2);% Large Target size
if opts.base_line.edge_method == 1
[U,center,~,~,~,~,LEupper,LElower,REupper,RElower,~,~,~] = LeftRightEdges_v03(opts.base_line, bag_data.lidar_target(tag_num).XYZIR_points, d);
elseif opts.base_line.edge_method == 2
[U, center, LEupper, LElower, REupper, RElower, ~, ~] = clickedToFindEdges_v02(opts.base_line, bag_data.lidar_target(tag_num).XYZIR_points, d);
elseif opts.base_line.edge_method == 3
[U, center, LEupper, LElower, REupper, RElower, ~, ~] = L1CostToFindEdges_v02(opts.base_line, bag_data.lidar_target(tag_num).XYZIR_points, d);
end
% Fail getting edges
min_edge_points = 2;
if size(LEupper, 2) < min_edge_points || size(LElower, 2) < min_edge_points || ...
size(REupper, 2) < min_edge_points || size(RElower, 2) < min_edge_points
% lidar target
bag_data.lidar_target(tag_num).ransac_normal.corners = [];
bag_data.lidar_target(tag_num).ransac_normal.corners = [];
bag_data.lidar_target(tag_num).ransac_normal.centroid = [];
bag_data.lidar_target(tag_num).ransac_normal.normal_vector = [];
bag_data.lidar_target(tag_num).ransac_normal.edges = [];
% camera target
bag_data.camera_target(tag_num).ransac_normal.corners = [];
% warning("Scan#%i has been skipped using edge method #%i of baseline.", scan, opts.base_line.edge_method)
return
end
[nL1,nL2,nL3]=size(LEupper);
LEupperall=reshape(LEupper,nL1,nL2*nL3);
I=find( (LEupperall(1,:)~= 10) & (LEupperall(2,:)~= 10));
edges.LU=U*[1 0; 0 1;0 0]*LEupperall(:,I) + center;
LEupperal_new = LEupperall(:, I);
[nL1,nL2,nL3]=size(LElower);
LElowerall=reshape(LElower,nL1,nL2*nL3);
I=find( (LElowerall(1,:)~= 10) & (LElowerall(2,:)~= 10) );
edges.LL=U*[1 0; 0 1;0 0]*LElowerall(:,I) + center;
LElowerall_new = LElowerall(:, I);
[nR1,nR2,nR3]=size(REupper);
REupperall=reshape(REupper,nR1,nR2*nR3);
I=find( (REupperall(1,:)~= 10) & (REupperall(2,:)~= 10) );
edges.RU=U*[1 0; 0 1; 0 0]*REupperall(:,I) + center;
REupperall_new = REupperall(:, I);
[nR1,nR2,nR3]=size(RElower);
RElowerall=reshape(RElower,nR1,nR2*nR3);
I=find( (RElowerall(1,:)~= 10) & (RElowerall(2,:)~= 10) );
edges.RL=U*[1 0; 0 1; 0 0]*RElowerall(:,I) + center;
RElowerall_new = RElowerall(:, I);
[x_TL, y_TL, modelInliers_TL] = ransacLine(LEupperal_new(1:2, :)', ransac_threshold);
[x_BL, y_BL, modelInliers_BL] = ransacLine(LElowerall_new(1:2, :)', ransac_threshold);
[x_TR, y_TR, modelInliers_TR] = ransacLine(REupperall_new(1:2, :)', ransac_threshold);
[x_BR, y_BR, modelInliers_BR] = ransacLine(RElowerall_new(1:2, :)', ransac_threshold);
cross_L=intersection(modelInliers_TL, modelInliers_BL);
cross_R=intersection(modelInliers_TR, modelInliers_BR);
cross_T=intersection(modelInliers_TL, modelInliers_TR);
cross_B=intersection(modelInliers_BR, modelInliers_BL);
cross_big_2d = [cross_L, cross_R, cross_T, cross_B];
cross_big_3d = U*[1 0; 0 1;0 0]*cross_big_2d + center;
cross_big_3d = [cross_big_3d; ones(1, size(cross_big_3d, 2))];
cross_big_3d = sortrows(cross_big_3d', 3, 'descend')';
[centroid, normals] = computeCentroidAndNormals(cross_big_3d);
bag_data.lidar_target(tag_num).ransac_normal.corners = cross_big_3d;
bag_data.lidar_target(tag_num).ransac_normal.centroid = centroid;
bag_data.lidar_target(tag_num).ransac_normal.normal_vector = normals;
bag_data.lidar_target(tag_num).ransac_normal.edges = edges;
if opts.base_line.show_results
% if 1
current_img_handle = opts.base_line.img_hangles(3);
hold(current_img_handle, 'on')
grid(current_img_handle, 'on')
axis(current_img_handle, 'equal')
scatter(current_img_handle, RElowerall_new(1,:), RElowerall_new(2,:), '.m'),
plot(current_img_handle, x_BL, y_BL, 'r-')
plot(current_img_handle, x_TL, y_TL, 'b-')
plot(current_img_handle, x_TR, y_TR, 'g-')
plot(current_img_handle, x_BR, y_BR, 'k-')
plot(current_img_handle, [cross_L(1) cross_B(1)], [cross_L(2) cross_B(2)], 'r-')
plot(current_img_handle, [cross_L(1) cross_T(1)], [cross_L(2) cross_T(2)], 'b-')
plot(current_img_handle, [cross_R(1) cross_T(1)], [cross_R(2) cross_T(2)], 'g-')
plot(current_img_handle, [cross_R(1) cross_B(1)], [cross_R(2) cross_B(2)], 'm-')
scatter(current_img_handle, cross_big_2d(1,:), cross_big_2d(2,:), 'd')
current_img_handle = opts.base_line.img_hangles(4);
scatter(current_img_handle, LEupperal_new(1,:), LEupperal_new(2,:),'.r')
hold(current_img_handle, 'on')
scatter(current_img_handle, LElowerall_new(1,:), LElowerall_new(2,:), '.b')
scatter(current_img_handle, REupperall_new(1,:), REupperall_new(2,:), '.g')
grid(current_img_handle, 'on')
axis(current_img_handle, 'equal')
scatter(current_img_handle, RElowerall_new(1,:), RElowerall_new(2,:), '.m'),
plot(current_img_handle, x_BL, y_BL, 'r-')
plot(current_img_handle, x_TL, y_TL, 'b-')
plot(current_img_handle, x_TR, y_TR, 'g-')
plot(current_img_handle, x_BR, y_BR, 'k-')
plot(current_img_handle, [cross_L(1) cross_B(1)], [cross_L(2) cross_B(2)], 'r-')
plot(current_img_handle, [cross_L(1) cross_T(1)], [cross_L(2) cross_T(2)], 'b-')
plot(current_img_handle, [cross_R(1) cross_T(1)], [cross_R(2) cross_T(2)], 'g-')
plot(current_img_handle, [cross_R(1) cross_B(1)], [cross_R(2) cross_B(2)], 'm-')
scatter(current_img_handle, cross_big_2d(1,:), cross_big_2d(2,:), 'd')
title(current_img_handle, "regressed edges")
set(get(current_img_handle, 'parent'),'visible','on');
current_img_handle = opts.base_line.img_hangles(2);
hold(current_img_handle, 'on')
scatter3(current_img_handle, cross_big_3d(1,1), cross_big_3d(2,1), cross_big_3d(3,1), 'or')
scatter3(current_img_handle, cross_big_3d(1,2), cross_big_3d(2,2), cross_big_3d(3,2), 'og')
scatter3(current_img_handle, cross_big_3d(1,3), cross_big_3d(2,3), cross_big_3d(3,3), 'ob')
scatter3(current_img_handle, cross_big_3d(1,4), cross_big_3d(2,4), cross_big_3d(3,4), 'om')
scatter3(current_img_handle, edges.LU(1,:), edges.LU(2,:), edges.LU(3,:), 'sr')
scatter3(current_img_handle, edges.LL(1,:), edges.LL(2,:), edges.LL(3,:), 'sg')
scatter3(current_img_handle, edges.RU(1,:), edges.RU(2,:), edges.RU(3,:), 'sb')
scatter3(current_img_handle, edges.RL(1,:), edges.RL(2,:), edges.RL(3,:), 'sm')
end
end