基于macOS的ORB_slam2的安装与运行

安装

1.在这之前要保证好eigen库，opencv库，Pangolin库已经安装好，
首先要将GitHub上的ORB_slam2的代码库克隆下来，打开Mac终端，输入

git clone https://github.com/gaoxiang12/ORBSLAM2_with_pointcloud_map.git

然后等待Mac自动解压

编译

1.首先进入该项目目录
2.创建build文件夹
3.进入build文件夹
4.执行编译
5.然后安装
6.构建第三方库与ORB_SLAM2（我的是没有安装ROS，所以执行build.sh）
代码如下：

mkdir build
cd build
cmake ..
make
sudo make install  
cd ~
chmod +x build.sh
./build.sh

数据集下载与处理

1.数据集的下载
我用的是慕尼黑工业大学数据集，这里直接附上网址，我选择的是前两个fr1/xyz和fr1/rpy（简单测试一下，数据集较小）

https://cvg.cit.tum.de/data/datasets/rgbd-dataset/download

2.关联rgb图像与深度图像，这里使用脚本Associate.py，在数据集的目录下新建此脚本，代码如下，粘贴并保存

#!/usr/bin/python
# Software License Agreement (BSD License)
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# Copyright (c) 2013, Juergen Sturm, TUM
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#  * Redistributions of source code must retain the above copyright
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#    copyright notice, this list of conditions and the following
#    disclaimer in the documentation and/or other materials provided
#    with the distribution.
#  * Neither the name of TUM nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
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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;
# 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.
#
# 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 = list(first_list.keys())
    second_keys = list(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])))

然后运行下述代码，记得将PATH_TO_SEQUENCE改成自己rgb.txt和depth.txt所在的位置

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt

执行完该代码会生成associations.txt文件,该文件记录了一些关联规则

运行

最后运行此代码，记得双击Vocabulary下面的压缩包进行解压得到ORBvoc.txt，X记得换做1，2，3其中的一个，PATH_TO_SEQUENCE_FOLDER也要改成数据集所在的路径，ASSOCIATIONS_FILE要改成associations.txt文件所在的路径

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

最后静等就可以啦
如下截图：

最后如果关于pangolin库，eigen库，opencv库的安装相关报错，还有下集老铁们！！！！