关于TUM数据集
2、验证回环检测算法，需要有人工标记回环的数据集。然而人工标记回环是很不方便的，我们会考虑根据标准轨迹计算回环。即，如果轨迹中有两个帧的位姿非常相近，就认为它们是回环。请根据
TUM数据集给出的标准轨迹，计算出一个数据集中的回环。这些回环的图像真的相似吗？
文章目录
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- TUM数据集资料_链接
- TUM 数据集 使用Tips
- 使用TUM数据集，并与标准轨迹进行比较
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- 数据集下载
- 仅下载轨迹
- ！！ 轨迹显示
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- 轨迹显示 Python3 仅处理了 位移信息
- 根据标准轨迹计算回环
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- 新建 .txt文件 touch CMakeLists.txt
- CMakeLists.txt文件 包含的内容
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TUM数据集资料_链接
！！高翔博客上的相关内容
TUM数据集网址：https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download
TUM 数据集 使用Tips
【Ctrl + ‘+’】放大字体。 博客园的字体有点小。
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乐趣：搞科研 + 码代码
associate.py
#!/usr/bin/python
# Software License Agreement (BSD License)
#
# Copyright (c) 2013, Juergen Sturm, TUM
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# disclaimer in the documentation and/or other materials provided
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# * Neither the name of TUM nor the names of its
# contributors may be used to endorse or promote products derived
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# 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;
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# 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.
#
# Requirements:
# sudo apt-get install python-argparse
"""
The Kinect provides the color and depth images in an un-synchronized way. This means that the set of time stamps from the color images do not intersect with those of the depth images. Therefore, we need some way of associating color images to depth images.
For this purpose, you can use the ''associate.py'' script. It reads the time stamps from the rgb.txt file and the depth.txt file, and joins them by finding the best matches.
"""
import argparse
import sys
import os
import numpy
def read_file_list(filename):
"""
Reads a trajectory from a text file.
File format:
The file format is "stamp d1 d2 d3 ...", where stamp denotes the time stamp (to be matched)
and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp.
Input:
filename -- File name
Output:
dict -- dictionary of (stamp,data) tuples
"""
file = open(filename)
data = file.read()
lines = data.replace(","," ").replace("\t"," ").split("\n")
list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
list = [(float(l[0]),l[1:]) for l in list if len(l)>1]
return dict(list)
def associate(first_list, second_list,offset,max_difference):
"""
Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim
to find the closest match for every input tuple.
Input:
first_list -- first dictionary of (stamp,data) tuples
second_list -- second dictionary of (stamp,data) tuples
offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)
max_difference -- search radius for candidate generation
Output:
matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))
"""
first_keys = first_list.keys()
second_keys = second_list.keys()
potential_matches = [(abs(a - (b + offset)), a, b)
for a in first_keys
for b in second_keys
if abs(a - (b + offset)) < max_difference]
potential_matches.sort()
matches = []
for diff, a, b in potential_matches:
if a in first_keys and b in second_keys:
first_keys.remove(a)
second_keys.remove(b)
matches.append((a, b))
matches.sort()
return matches
if __name__ == '__main__':
# parse command line
parser = argparse.ArgumentParser(description='''
This script takes two data files with timestamps and associates them
''')
parser.add_argument('first_file', help='first text file (format: timestamp data)')
parser.add_argument('second_file', help='second text file (format: timestamp data)')
parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')
parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
args = parser.parse_args()
first_list = read_file_list(args.first_file)
second_list = read_file_list(args.second_file)
matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))
if args.first_only:
for a,b in matches:
print("%f %s"%(a," ".join(first_list[a])))
else:
for a,b in matches:
print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))
# associate.py
python associate.py rgb.txt depth.txt
python associate.py rgb.txt depth.txt > associate.txt
draw_groundtruth.py
python draw_groundtruth.py
如何查找每个图像的真实位置呢？
python associate.py associate.txt groundtruth.txt > associate_with_groundtruth.txt
- 存 associate.py
- 试运行
使用TUM数据集，并与标准轨迹进行比较
数据集下载
TUM数据集网址：https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download
参考链接2
wget https://cvg.cit.tum.de/rgbd/dataset/freiburg1/rgbd_dataset_freiburg1_room.tgz
tar -xf rgbd_dataset_freiburg1_room.tgz
仅下载轨迹
TUM数据集网址：https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download
https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download
在数据集下载界面往下拉 或
点击进去，另存为。
https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download#freiburg1_room 或直接复制这个链接，另存即可。
需要把轨迹的.txt的前3行注释删掉
！！ 轨迹显示
trajectory.txt每一行的内容为： t i m e , t x , t y , t z , q x , q y , q z time, t_x,t_y,t_z,q_x, q_y, q_z time,tx,ty,tz,qx,qy,qz
time: 该位姿的记录时间
t \bm{t} t:平移
q \bm{q} q:旋转四元数
plotTrajectory.cpp
#include CMakeLists.txt
include_directories("/usr/include/eigen3")
find_package(Pangolin REQUIRED)
include_directories(${Pangolin_INCLUDE_DIRS})
add_executable(plotTrajectory plotTrajectory.cpp)
target_link_libraries(plotTrajectory ${Pangolin_LIBRARIES})
mkdir build && cd build
cmake ..
make
./plotTrajectory
GIF获取步骤
rgbd_dataset_freiburg1_room-groundtruth.txt
包含了 旋转信息
rgbd_dataset_freiburg1_desk-groundtruth.txt
desk TXT数据连接
xwininfo
byzanz-record -x 72 -y 64 -w 1848 -h 893 -d 10 --delay=5 -c /home/xixi/myGIF/test.gif
轨迹显示 Python3 仅处理了 位移信息
draw_groundtruth.py
#!/usr/bin/env python
# coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
f = open("./rgbd_dataset_freiburg1_room-groundtruth.txt")
x = []
y = []
z = []
for line in f:
if line[0] == '#': # 这里跳过了 注释行
continue
data = line.split()
x.append( float(data[1] ) )
y.append( float(data[2] ) )
z.append( float(data[3] ) )
ax = plt.subplot( 111, projection='3d')
ax.plot(x,y,z)
plt.show()
命令行：
python3 draw_groundtruth.py
根据标准轨迹计算回环
tum.cpp
#include CMakeLists.txt
cmake_minimum_required(VERSION 2.8)
project(tum) # 输出文件名
include_directories("/usr/include/eigen3")
find_package(Pangolin REQUIRED)
include_directories(${Pangolin_INCLUDE_DIRS})
add_executable(tum tum.cpp)
target_link_libraries(tum ${Pangolin_LIBRARIES})
命令行窗口指令：
mkdir build # 若是已建有，跳过这步
cd build
cmake ..
make
./tum
轨迹.txt文件里的时间和图片的不太一致，暂时不清楚怎么对应。
delta = 15:
delta = 20:
delta = 10： 获得更多组结果。
由于 图片读取间隔的原因，图片名称不完全对应。。
新建 .txt文件 touch CMakeLists.txt
在待创建.txt文件的目录下打开命令行窗口。
touch CMakeLists.txt
其它类型文件 亦可用。
CMakeLists.txt文件 包含的内容
