新手非ROS YOLO_ORB_SLAM3运行经验

1.源码下载地址：

https://github.com/YWL0720/YOLO_ORB_SLAM3.git

2.环境设置：

参考这位博主：https://blog.youkuaiyun.com/qingniyaojizhuwo/article/details/137231654?ops_request_misc=%257B%2522request%255Fid%2522%253A%25225D51F819-CDB8-4412-BFDE-354E3DA7A268%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=5D51F819-CDB8-4412-BFDE-354E3DA7A268&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-2-137231654-null-null.142^v100^pc_search_result_base6&utm_term=orb-slam3%E7%BB%93%E5%90%88yolov5&spm=1018.2226.3001.4187

3.下载libtorch

wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcpu.zip
unzip libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcpu.zip
mv libtorch/ PATH/YOLO_ORB_SLAM3/Thirdparty/

mv libtorch/ PATH/YOLO_ORB_SLAM3/Thirdparty/这步是将下载的libtorch移动进你下载的YOLO_ORB_SLAM3目录下Thirdparty中，也可以自主复制粘贴。如下图所示，libtorch在Thirdparty下。

4.Build

cd YOLO_ORB_SLAM3
chmod +x build.sh
./build.sh

       这一步我运行的时候没有出错。但是在ROS情况Build运行的时候出现错误了，如果后续解决了，我会再出一篇如何解决。

5.运行代码

5.1 下载数据集

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

       在YOLO_ORB_SLAM3目录下创建一个dataset文件，将下载的数据集解压缩放到dataset下。如下图所示。

5.2 建立dataset文件

当时没进行这一步，直接按照github上面步骤运行，没运行出来，这部分参考的这篇

https://blog.youkuaiyun.com/2302_80099075/article/details/141746138

在YOLO_ORB_SLAM3/Examples/RGB-D文件下新建一个associate.py文件。

打开associate.py文件，将以下代码复制进去

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")
    #if remove_bounds:
    #    lines = lines[100:-100]
    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])))

5.3 运行

在YOLO_ORB_SLAM3下打开终端，执行以下命令。

python3 ./Examples/RGB-D/associate.py ./dataset/rgbd_dataset_freiburg3_walking_xyz/depth.txt ./dataset/rgbd_dataset_freiburg3_walking_xyz/rgb.txt >./dataset/rgbd_dataset_freiburg3_walking_xyz/associations.txt

这条命令的作用是使用 associate.py 脚本将深度图像文件和RGB图像文件关联起来，并将关联结果输出到一个新的文件中。

./Examples/RGB-D/associate.py 指定了脚本文件的路径。这里的 ./ 表示当前目录。

./dataset/rgbd_dataset_freiburg3_walking_xyz/depth.txt这是深度图像文件列表的路径。这个文件通常包含了深度图像文件的名称列表，每行一个文件名。

./dataset/rgbd_dataset_freiburg3_walking_xyz/rgb.txt：这是RGB图像文件列表的路径。这个文件同样包含了RGB图像文件的名称列表，每行一个文件名。

再接着执行下面的命令：

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM3.yaml ./dataset/rgbd_dataset_freiburg3_walking_xyz/ ./dataset/rgbd_dataset_freiburg3_walking_xyz/associations.txt 

会出现以下画面。切记，路径一定要填准确。