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computeConfidenceOfRange.m
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computeConfidenceOfRange.m
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%{
* Copyright (C) 2013-2025, 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 confidence_of_range = computeConfidenceOfRange(P, bag_data, validation_indices, method, refinement)
num_senes = length(validation_indices);
for scene = 1:num_senes % which dataset
current_index = validation_indices(scene);
num_scan = length(bag_data(current_index).scans(:));
confidence_of_range(scene).bagfile = bag_data(current_index).bagfile;
for scan_num = 1:num_scan
num_tag = size(bag_data(current_index).scans(scan_num).lidar_target, 2);
for tag_num = 1:num_tag % which tag in this dataset
% refinement and no-refinement should be the same
if isempty(bag_data(current_index).scans(scan_num).lidar_target(tag_num).(method).corners)
continue
else
if strcmpi(refinement, 'no_refinement')
current_corners_X = [bag_data(current_index).scans(scan_num).lidar_target(tag_num).(method).corners];
H_LT = bag_data(current_index).scans(scan_num).lidar_target(tag_num).(method).H_LT;
else
current_corners_X = [bag_data(current_index).scans(scan_num).lidar_target(tag_num).(method).refined_corners];
H_LT = bag_data(current_index).scans(scan_num).lidar_target(tag_num).(method).refined_H_LC;
end
num_corners = size(H_LT, 2) / 4;
current_corners_Y = [bag_data(current_index).scans(scan_num).camera_target(tag_num).(method).corners];
squared_sum = verifyCornerAccuracy(current_corners_X(:, 1:4), current_corners_Y(:, 1:4), P);
confidence_of_range(scene).scans(scan_num).targets(tag_num).RMSE = sqrt(squared_sum / num_corners);
confidence_of_range(scene).scans(scan_num).targets(tag_num).distance = norm(H_LT(1:3, 4));
confidence_of_range(scene).scans(scan_num).targets(tag_num).rpy = rad2deg(rotm2eul(H_LT(1:3, 1:3), 'xyz'));
end
end
end
end
end