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getAll4CornersReturnHLT.m
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getAll4CornersReturnHLT.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, [email protected]. 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, H_LT] = getAll4CornersReturnHLT(tag_num, opt, path, bag_data, opts)
% scan_num: scan number of these corner
% num_scan: how many scans accumulated to get the corners
% function [LiDARTag, AprilTag, H_LT] = get4CornersReturnHLT(tag_num, opt, mat_file_path, pc_mat_file, bag_file, target_len, pc_iter, num_scan)
% if show.lidar_target_optimization
% target_len = bag_data.lidar_target(tag_num).tag_size;
% pc = loadPointCloud(path.mat_file_path, bag_data.lidar_target(tag_num).pc_file);
% for pc_iter = 1:5
% X = getPayload(pc, pc_iter, opts.num_scan);
% opt_temp_draw = opt.H_TL;
% [X_clean, scan_total_draw(pc_iter).clean_up] = cleanLiDARTargetWithOneDataSet(X, target_len, opt_temp_draw);
%
% cost
% opt_temp_draw = optimizeCost(opt_temp_draw, X_clean, target_len, scan_total_draw(pc_iter).clean_up.std/2);
% target_lidar = [0 -target_len/2 -target_len/2 1;
% 0 -target_len/2 target_len/2 1;
% 0 target_len/2 target_len/2 1;
% 0 target_len/2 -target_len/2 1]';
%
% corners = opt_temp_draw.H_opt \ target_lidar;
% corners = sortrows(corners', 3, 'descend')';
% centroid = mean(corners(1:3,:), 2);
% normals = cross(corners(1:3,1)-corners(1:3,2), corners(1:3,1)-corners(1:3,3));
% normals = normals/(norm(normals));
% if normals(1) > 0
% normals = -normals;
% end
% end
% end
pc = loadPointCloud(path.mat_file_path, bag_data.lidar_target(tag_num).pc_file);
num_scan_total = size(pc, 1) - opts.num_scan;
target_len = bag_data.lidar_target(tag_num).tag_size;
if opts.optimizeAllCorners
scan_total(num_scan_total) = struct();
scan_total(num_scan_total).clean_up = [];
scan_total(num_scan_total).corners = [];
scan_total(num_scan_total).four_corners_line = [];
scan_total(num_scan_total).pc_points_original = [];
scan_total(num_scan_total).pc_points = [];
scan_total(num_scan_total).centroid = [];
scan_total(num_scan_total).normal_vector = [];
scan_total(num_scan_total).H = []; %% H_LT
disp("------------------------------------------------------------")
disp("Optimizing LiDAR vertices using PARFOR, the numbers WILL NOT BE in ORDERED")
disp("------------------------------------------------------------")
tic
parfor pc_iter = 1:num_scan_total
if mod(pc_iter, 10) == 0 || pc_iter == num_scan_total || pc_iter == 1
fprintf("--- Working on scan: %i/%i\n", pc_iter, num_scan_total)
end
% fprintf("--- Working on scan: %i/%i\n", pc_iter, num_scan_total)
X = getPayload(pc, pc_iter, opts.num_scan);
opt_temp = opt.H_TL;
[X_clean, scan_total(pc_iter).clean_up] = cleanLiDARTargetWithOneDataSet(X, target_len, opt_temp);
% cost
opt_temp = optimizeCost(opt_temp, X_clean, target_len, scan_total(pc_iter).clean_up.std/2);
target_lidar = [0 -target_len/2 -target_len/2 1;
0 -target_len/2 target_len/2 1;
0 target_len/2 target_len/2 1;
0 target_len/2 -target_len/2 1]';
corners = opt_temp.H_opt \ target_lidar;
corners = sortrows(corners', 3, 'descend')';
[centroid, normals] = computeCentroidAndNormals(corners);
scan_total(pc_iter).corners = corners;
scan_total(pc_iter).four_corners_line = point3DToLineForDrawing(corners);
scan_total(pc_iter).pc_points_original = X;
scan_total(pc_iter).pc_points = X_clean;
scan_total(pc_iter).centroid = centroid;
scan_total(pc_iter).normal_vector = normals;
scan_total(pc_iter).H = inv(opt_temp.H_opt);
end
time_elp = toc;
fprintf("Spent %f on optimizing corners of %i scans", time_elp, num_scan_total)
%
for pc_iter = 1:num_scan_total
for j = 1:num_scan_total
if j == pc_iter
similarity_table(pc_iter).scan(j).diff = 1000;
else
difference = scan_total(j).H \ scan_total(pc_iter).H;
logR = logm(difference(1:3,1:3));
v = [-logR(1,2) logR(1,3) -logR(2,3) difference(1:3,4)'];
similarity_table(pc_iter).scan(j).diff = norm(v);
end
end
similarity_table(pc_iter).mins = sum(mink([similarity_table(pc_iter).scan(:).diff], opts.num_lidar_target_pose));
end
if ~exist(path.save_dir, 'dir')
mkdir(path.save_dir)
end
save(path.save_dir + extractBetween(bag_data.bagfile,"",".bag") + '_' + tag_num + '_' + '_all_scan_corners.mat', 'similarity_table', 'scan_total');
else
load(path.load_all_vertices + extractBetween(bag_data.bagfile,"",".bag") + '_' + tag_num + '_' + '_all_scan_corners.mat');
end
if opts.use_top_consistent_vertices
[~, chosen_scan] = min([similarity_table(:).mins]);
[~, chosen_scans] = mink([similarity_table(chosen_scan).scan(:).diff], opts.num_lidar_target_pose);
else
chosen_scans = randperm(num_scan_total, opts.num_lidar_target_pose);
end
H_LT = [];
for scan_num = 1:opts.num_lidar_target_pose
if opts.use_top_consistent_vertices || opts.randperm_to_fine_vertices
current_scan = chosen_scans(scan_num);
else
current_scan = opts.num_scan*(scan_num-1) + 1;
end
bag_data.lidar_target(tag_num).scan(scan_num).corners = scan_total(current_scan).corners;
bag_data.lidar_target(tag_num).scan(scan_num).four_corners_line = point3DToLineForDrawing(scan_total(current_scan).corners);
bag_data.lidar_target(tag_num).scan(scan_num).pc_points_original = scan_total(current_scan).pc_points_original;
bag_data.lidar_target(tag_num).scan(scan_num).pc_points = scan_total(current_scan).pc_points;
bag_data.lidar_target(tag_num).scan(scan_num).centroid = scan_total(current_scan).centroid;
bag_data.lidar_target(tag_num).scan(scan_num).normal_vector = scan_total(current_scan).normal_vector;
bag_data.lidar_target(tag_num).scan(scan_num).H = scan_total(current_scan).H;
H_LT = [H_LT scan_total(current_scan).H];
end
if opts.refineAllCorners
num_scan_total = size(scan_total, 2) - opts.num_scan;
refinement_scan_total(num_scan_total) = struct();
refinement_scan_total(num_scan_total).original_corners = [];
refinement_scan_total(num_scan_total).refined_corners = [];
refinement_scan_total(num_scan_total).refined_centroid = [];
refinement_scan_total(num_scan_total).refined_normal_vector = [];
refinement_scan_total(num_scan_total).refined_H = [];
refinement_scan_total(num_scan_total).refined_P = [];
disp("\n------------------------------------------------------------")
disp("Refining LiDAR vertices using PARFOR, the numbers WILL NOT BE in ORDERED")
disp("------------------------------------------------------------")
tic
parfor pc_iter = 1:num_scan_total
if mod(pc_iter, 10) == 0 || pc_iter == num_scan_total || pc_iter == 1
fprintf("--- Working on refinement scan: %i/%i\n", pc_iter, num_scan_total)
end
if opts.use_top_consistent_vertices
[~, chosen_scan] = min([similarity_table(:).mins]);
[~, chosen_scans] = mink([similarity_table(chosen_scan).scan(:).diff], opts.num_lidar_target_pose);
else
chosen_scans = randperm(num_scan_total, opts.num_lidar_target_pose);
end
X_train = [scan_total(pc_iter:pc_iter + opts.num_lidar_target_pose - 1).corners];
Y_train = [repmat(bag_data.camera_target(tag_num).corners, 1, opts.num_lidar_target_pose)];
H_LT = [scan_total(pc_iter:pc_iter + opts.num_lidar_target_pose - 1).H];
switch opts.calibration_method
case "4 points"
for i = 0: opts.num_refinement-1
% disp('---------------------')
% disp(' Optimizing H_LC ...')
% disp('---------------------')
%
%
% disp('---------------------')
% disp('--- SR_H_LC ...')
% disp('---------------------')
% square with refinement
show_pnp_numerical_result = 0;
[SR_H_LC, SR_P, SR_opt_total_cost, SR_final, SR_All] = optimize4Points(opt.H_LC.rpy_init, ...
X_train, Y_train, ...
opt.intrinsic_matrix, ...
show_pnp_numerical_result);
if i == opts.num_refinement-1
break;
else
% disp('------------------')
% disp(' Refining SR_H_LC ...')
% disp('------------------')
X_refined_corners = [];
for scan_num = 1:opts.num_lidar_target_pose
current_scan = pc_iter + scan_num;
X_train_tmp = [scan_total(current_scan).corners];
Y_train_tmp = [bag_data.camera_target(tag_num).corners];
H_LT = scan_total(current_scan).H;
tag_size = bag_data.lidar_target(tag_num).tag_size;
refinement_scan_total(pc_iter).refined_corners = regulizedFineTuneEachLiDARTagPose(tag_size, ...
X_train_tmp, Y_train_tmp, H_LT, SR_P, ...
show_pnp_numerical_result);
refinement_scan_total(pc_iter).original_corners = X_train_tmp;
X_refined_corners = [X_refined_corners, refinement_scan_total(pc_iter).refined_corners];
X_refined_corners = sortrows(X_refined_corners', 3, 'descend')';
[centroid, normals] = computeCentroidAndNormals(X_refined_corners);
end
X_train = X_refined_corners;
% X_not_square_refinement = regulizedFineTuneKaessCorners(X_not_square_refinement, Y_base_line,...
% X_base_line_edge_points, NSR_P, ...
% opts.correspondance_per_pose, show_pnp_numerical_result);
end
refinement_scan_total(pc_iter).refined_centroid = centroid;
refinement_scan_total(pc_iter).refined_normal_vector = normals;
refinement_scan_total(pc_iter).refined_H = SR_H_LC;
refinement_scan_total(pc_iter).refined_P = SR_P;
end
case "IoU"
disp("refinement for IoU hasn't been implenmented yet. Will come soon!")
end
end
time_elp = toc;
fprintf("Spent %f on refining corners of %i scans", time_elp, num_scan_total)
if ~exist(path.save_dir, 'dir')
mkdir(path.save_dir)
end
% save(path.save_dir + extractBetween(bag_data.bagfile,"",".bag") + '_' + tag_num + '_' + '_all_scan_refined_corners.mat', 'refinement_scan_total');
save(path.save_dir + extractBetween(bag_data.bagfile,"",".bag") + '_' + tag_num + '_' + path.event_name + '_all_scan_refined_corners.mat', 'refinement_scan_total');
else
load(path.load_all_vertices + extractBetween(bag_data.bagfile,"",".bag") + '_' + tag_num + '_' + path.event_name + '_all_scan_refined_corners.mat');
end
for scan_num = 1:opts.num_lidar_target_pose
if opts.use_top_consistent_vertices || opts.randperm_to_fine_vertices
current_scan = chosen_scans(scan_num);
else
current_scan = opts.num_scan*(scan_num-1) + 1;
end
bag_data.lidar_target(tag_num).scan(scan_num).refined_corners = refinement_scan_total(current_scan).refined_corners;
bag_data.lidar_target(tag_num).scan(scan_num).refined_centroid = refinement_scan_total(current_scan).refined_centroid;
bag_data.lidar_target(tag_num).scan(scan_num).refined_normal_vector = refinement_scan_total(current_scan).refined_normal_vector;
bag_data.lidar_target(tag_num).scan(scan_num).refined_H = refinement_scan_total(current_scan).refined_H;
bag_data.lidar_target(tag_num).scan(scan_num).refined_P = refinement_scan_total(current_scan).refined_P;
end
end