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cleanLiDARTargetCore.m
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cleanLiDARTargetCore.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 [X_clean, X_ref, X_std, L_infinity, N] = cleanLiDARTargetCore(opt, X, target_len)
N = 4; % clean up using N std for x axis
M = 3; % clean up using M std for y, z axis
opt = optimizeCost(opt, X, target_len, 0.001);
X_ref = opt.H_opt * X;
% distance = sum(X_ref(1:3,:), 1); % L1
L_infinity = max(abs(X_ref(1:3,:))); % L infinity
K = find(L_infinity < (target_len/2)*1.025);
X_ref_clean_yz = X_ref(:, K); % clean up y and z axis
L_infinity = max(X_ref_clean_yz(1:3,:));
K_center = find(L_infinity < target_len/4);
X_std = std(X_ref_clean_yz(:, K_center), 1, 2);
Q = find(abs(X_ref_clean_yz(1,:)) < N*(X_std(1))); % clean up x with 2 std
X_ref_clean = X_ref_clean_yz(:, Q);
X_clean = inv(opt.H_opt) * X_ref_clean;
end