1、TUM evaluate_ate.py, 在原有基礎上，增加了輸出每一幀ATE
以下代碼增加了額外打註釋，可以輔助理解ATE的求解方式。
#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
import numpy
import argparse
import associate
#一般數據的第一幀的相機的位姿設定爲單位矩陣，但是grounftruth的第一幀未必是算法估計位姿打第一幀，
#因此兩個軌跡很有不能不在同一個座標系，因此，首先需要將估計的軌跡和grounftruth軌跡align到統一座標系下，方法類似ICP.
def align(model,data):
"""Align two trajectories using the method of Horn (closed-form).
Input:
model -- first trajectory (3xn)
data -- second trajectory (3xn)
Output:
rot -- rotation matrix (3x3)
trans -- translation vector (3x1)
trans_error -- translational error per point (1xn)
"""
numpy.set_printoptions(precision=3,suppress=True)
model_zerocentered = model - model.mean(1)
data_zerocentered = data - data.mean(1)
W = numpy.zeros( (3,3) )
for column in range(model.shape[1]):
W += numpy.outer(model_zerocentered[:,column],data_zerocentered[:,column])
U,d,Vh = numpy.linalg.linalg.svd(W.transpose())
S = numpy.matrix(numpy.identity( 3 ))
if(numpy.linalg.det(U) * numpy.linalg.det(Vh)<0):
S[2,2] = -1
rot = U*S*Vh
trans = data.mean(1) - rot * model.mean(1)
model_aligned = rot * model + trans
#alignment_error 維度:3 * n, 每一列是一個向量，即軌跡中兩個對應點的絕對估計誤差。
alignment_error = model_aligned - data
#trans_error 維度:3 * n, 每一列是一個向量，即軌跡中兩個對應點的距離。
trans_error = numpy.sqrt(numpy.sum(numpy.multiply(alignment_error,alignment_error),0)).A[0]
return rot,trans,trans_error
#分別繪製groundtruth和估計位姿的曲線，只是繪製了每個點的x,y座標，省去了第三維，因此是一個平面圖
def plot_traj(ax,stamps,traj,style,color,label):
"""
Plot a trajectory using matplotlib.
Input:
ax -- the plot
stamps -- time stamps (1xn)
traj -- trajectory (3xn)
style -- line style
color -- line color
label -- plot legend
"""
stamps.sort()
interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
x = []
y = []
last = stamps[0]
for i in range(len(stamps)):
if stamps[i]-last < 2*interval:
x.append(traj[i][0])
y.append(traj[i][1])
elif len(x)>0:
ax.plot(x,y,style,color=color,label=label)
label=""
x=[]
y=[]
last= stamps[i]
if len(x)>0:
ax.plot(x,y,style,color=color,label=label)
if __name__=="__main__":
# parse command line
parser = argparse.ArgumentParser(description='''
This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
''')
parser.add_argument('first_file', help='ground truth trajectory (format: timestamp tx ty tz qx qy qz qw)')
parser.add_argument('second_file', help='estimated trajectory (format: timestamp tx ty tz qx qy qz qw)')
parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
parser.add_argument('--scale', help='scaling factor for the second trajectory (default: 1.0)',default=1.0)
parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
parser.add_argument('--save', help='save aligned second trajectory to disk (format: stamp2 x2 y2 z2)')
parser.add_argument('--save_per_transerr', help='save tanserror of each frame (format: stamp error)')
parser.add_argument('--save_associations', help='save associated first and aligned second trajectory to disk (format: stamp1 x1 y1 z1 stamp2 x2 y2 z2)')
parser.add_argument('--plot', help='plot the first and the aligned second trajectory to an image (format: png)')
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')
args = parser.parse_args()
first_list = associate.read_file_list(args.first_file)
second_list = associate.read_file_list(args.second_file)
matches = associate.associate(first_list, second_list,float(args.offset),float(args.max_difference))
#print(matches)
if len(matches)<2:
sys.exit("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory! Did you choose the correct sequence?")
#shape:3*n
first_xyz = numpy.matrix([[float(value) for value in first_list[a][0:3]] for a,b in matches]).transpose()
second_xyz = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for a,b in matches]).transpose()
#the 1st frmae is Identity Matrix, but the 1st frame may be different in groundth and estimate
rot,trans,trans_error = align(second_xyz,first_xyz)
second_xyz_aligned = rot * second_xyz + trans
first_stamps = first_list.keys()
first_stamps.sort()
first_xyz_full = numpy.matrix([[float(value) for value in first_list[b][0:3]] for b in first_stamps]).transpose()
second_stamps = second_list.keys()
second_stamps.sort()
second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for b in second_stamps]).transpose()
second_xyz_full_aligned = rot * second_xyz_full + trans
if args.verbose:
print "compared_pose_pairs %d pairs"%(len(trans_error))
print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
print "absolute_translational_error.mean %f m"%numpy.mean(trans_error)
print "absolute_translational_error.median %f m"%numpy.median(trans_error)
print "absolute_translational_error.std %f m"%numpy.std(trans_error)
print "absolute_translational_error.min %f m"%numpy.min(trans_error)
print "absolute_translational_error.max %f m"%numpy.max(trans_error)
else:
print "%f"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
if args.save_associations:
file = open(args.save_associations,"w")
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)]))
file.close()
if args.save:
file = open(args.save,"w")
file.write("\n".join(["%f "%stamp+" ".join(["%f"%d for d in line]) for stamp,line in zip(second_stamps,second_xyz_full_aligned.transpose().A)]))
file.close()
#分別輸出每一幀的絕對位姿誤差
if args.save_per_transerr:
file = open(args.save_per_transerr,"w")
for i in range(0,len(matches)):
stamp = "%.6f"% matches[i][1]
tanserr = trans_error[i]
file.write(stamp+"\t"+"%f"%tanserr+"\n")
file.close()
#繪製groundtruth和估計位姿誤差
if args.plot:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.patches import Ellipse
fig = plt.figure()
ax = fig.add_subplot(111)
plot_traj(ax,first_stamps,first_xyz_full.transpose().A,'-',"black","ground truth")
plot_traj(ax,second_stamps,second_xyz_full_aligned.transpose().A,'-',"blue","estimated")
label="difference"
for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A):
ax.plot([x1,x2],[y1,y2],'-',color="red",label=label)
label=""
ax.legend()
ax.set_xlabel('x [m]')
ax.set_ylabel('y [m]')
plt.savefig(args.plot,dpi=90)
2、python自動調用C++可執行程序腳本
import os
import shutil
voca_path = "../../Vocabulary/ORBvoc.txt"
tum_path = "./TUM3.yaml"
data_path = ["",
"1-freiburg3_walking_xyz",
"2-freiburg3_walking_halfsphere",
"3-freiburg3_walking_static",
"4-freiburg3_walking_rpy",
"5-freiburg3_sitting_xyz"]
gtruth = ["",
"results/groundtruth/groundtruth1.txt",
"results/groundtruth/groundtruth2.txt",
"results/groundtruth/groundtruth3.txt",
"results/groundtruth/groundtruth4.txt",
"results/groundtruth/groundtruth5.txt"]
for i in range(1,6) :
for j in range(1,21) :
os.system(r'./rgbd_tum '+ voca_path + ' ' + tum_path +' ../../data/' + data_path[i] + ' ../../data/' + data_path[i] + '/associate.txt')
os.system(r'python results/evaluate_ate.py ' + gtruth[i] + ' results/cameraTrajectory.txt --verbose --save_per_transerr err.txt')
os.system(r'python statistic_dyna_err.py dynapoints_cnt.txt err.txt')
dir_name = str(i)+"-"+str(j);
os.mkdir("dyna_affect_err/"+dir_name)
shutil.copy("err.png", "dyna_affect_err/" + dir_name)
shutil.copy("dynapoints_cnt.txt", "dyna_affect_err/" + dir_name)
shutil.copy("err.txt", "dyna_affect_err/" + dir_name)
shutil.copy("err.png", "dyna_affect_err/err" + dir_name + ".png")
