230 lines
10 KiB
Python
230 lines
10 KiB
Python
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# Modified by Raul Mur-Artal
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# Automatically compute the optimal scale factor for monocular VO/SLAM.
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# Software License Agreement (BSD License)
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#
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# Copyright (c) 2013, Juergen Sturm, TUM
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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# * Neither the name of TUM nor the names of its
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# contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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#
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# Requirements:
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# sudo apt-get install python-argparse
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"""
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This script computes the absolute trajectory error from the ground truth
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trajectory and the estimated trajectory.
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"""
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import sys
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import numpy
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import argparse
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import associate
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def align(model,data):
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"""Align two trajectories using the method of Horn (closed-form).
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Input:
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model -- first trajectory (3xn)
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data -- second trajectory (3xn)
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Output:
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rot -- rotation matrix (3x3)
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trans -- translation vector (3x1)
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trans_error -- translational error per point (1xn)
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"""
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numpy.set_printoptions(precision=3,suppress=True)
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model_zerocentered = model - model.mean(1)
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data_zerocentered = data - data.mean(1)
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W = numpy.zeros( (3,3) )
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for column in range(model.shape[1]):
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W += numpy.outer(model_zerocentered[:,column],data_zerocentered[:,column])
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U,d,Vh = numpy.linalg.linalg.svd(W.transpose())
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S = numpy.matrix(numpy.identity( 3 ))
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if(numpy.linalg.det(U) * numpy.linalg.det(Vh)<0):
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S[2,2] = -1
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rot = U*S*Vh
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rotmodel = rot*model_zerocentered
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dots = 0.0
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norms = 0.0
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for column in range(data_zerocentered.shape[1]):
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dots += numpy.dot(data_zerocentered[:,column].transpose(),rotmodel[:,column])
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normi = numpy.linalg.norm(model_zerocentered[:,column])
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norms += normi*normi
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s = float(dots/norms)
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transGT = data.mean(1) - s*rot * model.mean(1)
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trans = data.mean(1) - rot * model.mean(1)
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model_alignedGT = s*rot * model + transGT
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model_aligned = rot * model + trans
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alignment_errorGT = model_alignedGT - data
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alignment_error = model_aligned - data
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trans_errorGT = numpy.sqrt(numpy.sum(numpy.multiply(alignment_errorGT,alignment_errorGT),0)).A[0]
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trans_error = numpy.sqrt(numpy.sum(numpy.multiply(alignment_error,alignment_error),0)).A[0]
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return rot,transGT,trans_errorGT,trans,trans_error, s
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def plot_traj(ax,stamps,traj,style,color,label):
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"""
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Plot a trajectory using matplotlib.
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Input:
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ax -- the plot
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stamps -- time stamps (1xn)
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traj -- trajectory (3xn)
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style -- line style
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color -- line color
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label -- plot legend
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"""
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stamps.sort()
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interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
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x = []
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y = []
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last = stamps[0]
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for i in range(len(stamps)):
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if stamps[i]-last < 2*interval:
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x.append(traj[i][0])
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y.append(traj[i][1])
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elif len(x)>0:
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ax.plot(x,y,style,color=color,label=label)
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label=""
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x=[]
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y=[]
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last= stamps[i]
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if len(x)>0:
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ax.plot(x,y,style,color=color,label=label)
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if __name__=="__main__":
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# parse command line
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parser = argparse.ArgumentParser(description='''
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This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
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''')
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parser.add_argument('first_file', help='ground truth trajectory (format: timestamp tx ty tz qx qy qz qw)')
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parser.add_argument('second_file', help='estimated trajectory (format: timestamp tx ty tz qx qy qz qw)')
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parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
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parser.add_argument('--scale', help='scaling factor for the second trajectory (default: 1.0)',default=1.0)
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parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 10000000 ns)',default=20000000)
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parser.add_argument('--save', help='save aligned second trajectory to disk (format: stamp2 x2 y2 z2)')
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parser.add_argument('--save_associations', help='save associated first and aligned second trajectory to disk (format: stamp1 x1 y1 z1 stamp2 x2 y2 z2)')
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parser.add_argument('--plot', help='plot the first and the aligned second trajectory to an image (format: png)')
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parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the RMSE absolute translational error in meters after alignment will be printed)', action='store_true')
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parser.add_argument('--verbose2', help='print scale eror and RMSE absolute translational error in meters after alignment with and without scale correction', action='store_true')
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args = parser.parse_args()
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first_list = associate.read_file_list(args.first_file, False)
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second_list = associate.read_file_list(args.second_file, False)
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matches = associate.associate(first_list, second_list,float(args.offset),float(args.max_difference))
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if len(matches)<2:
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sys.exit("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory! Did you choose the correct sequence?")
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first_xyz = numpy.matrix([[float(value) for value in first_list[a][0:3]] for a,b in matches]).transpose()
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second_xyz = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for a,b in matches]).transpose()
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dictionary_items = second_list.items()
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sorted_second_list = sorted(dictionary_items)
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second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in sorted_second_list[i][1][0:3]] for i in range(len(sorted_second_list))]).transpose() # sorted_second_list.keys()]).transpose()
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rot,transGT,trans_errorGT,trans,trans_error, scale = align(second_xyz,first_xyz)
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second_xyz_aligned = scale * rot * second_xyz + trans
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second_xyz_notscaled = rot * second_xyz + trans
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second_xyz_notscaled_full = rot * second_xyz_full + trans
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first_stamps = first_list.keys()
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first_stamps.sort()
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first_xyz_full = numpy.matrix([[float(value) for value in first_list[b][0:3]] for b in first_stamps]).transpose()
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second_stamps = second_list.keys()
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second_stamps.sort()
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second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for b in second_stamps]).transpose()
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second_xyz_full_aligned = scale * rot * second_xyz_full + trans
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if args.verbose:
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print "compared_pose_pairs %d pairs"%(len(trans_error))
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print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
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print "absolute_translational_error.mean %f m"%numpy.mean(trans_error)
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print "absolute_translational_error.median %f m"%numpy.median(trans_error)
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print "absolute_translational_error.std %f m"%numpy.std(trans_error)
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print "absolute_translational_error.min %f m"%numpy.min(trans_error)
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print "absolute_translational_error.max %f m"%numpy.max(trans_error)
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print "max idx: %i" %numpy.argmax(trans_error)
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else:
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# print "%f, %f " % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale)
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# print "%f,%f" % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale)
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print "%f,%f,%f" % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale, numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT)))
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# print "%f" % len(trans_error)
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if args.verbose2:
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print "compared_pose_pairs %d pairs"%(len(trans_error))
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print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
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print "absolute_translational_errorGT.rmse %f m"%numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT))
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if args.save_associations:
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file = open(args.save_associations,"w")
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file.write("\n".join(["%f %f %f %f %f %f %f %f"%(a,x1,y1,z1,b,x2,y2,z2) for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A)]))
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file.close()
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if args.save:
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file = open(args.save,"w")
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file.write("\n".join(["%f "%stamp+" ".join(["%f"%d for d in line]) for stamp,line in zip(second_stamps,second_xyz_notscaled_full.transpose().A)]))
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file.close()
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if args.plot:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.pylab as pylab
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from matplotlib.patches import Ellipse
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fig = plt.figure()
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ax = fig.add_subplot(111)
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plot_traj(ax,first_stamps,first_xyz_full.transpose().A,'-',"black","ground truth")
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plot_traj(ax,second_stamps,second_xyz_full_aligned.transpose().A,'-',"blue","estimated")
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label="difference"
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for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A):
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ax.plot([x1,x2],[y1,y2],'-',color="red",label=label)
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label=""
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ax.legend()
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ax.set_xlabel('x [m]')
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ax.set_ylabel('y [m]')
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plt.axis('equal')
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plt.savefig(args.plot,format="pdf")
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