One 、 use sdk Recording rosbag

Two 、 Play rosbag And use rviz see topic, Write down the rgb and depth Stream title

3、 ... and 、 Use the following script （python2 instead of 3） preservation rgb and depth Pictures are generated at the same time rgb.txt、depth.txt

You can put alias python='/usr/bin/python2.7’ Write in bashrc, Remember to comment out .

Four 、 Use this script associate.py Generate associate.txt

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])))

python2 associate.py depth.txt rgb.txt > associate.txt

5、 ... and 、 test

/Examples/RGB-D/rgbd_tum ./Vocabulary/ORBvoc.txt ./Examples/RGB-D/D435i.yaml ./dataset/ ./dataset/associate.txt

# A copy of D435i.yaml

%YAML:1.0

#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------

# Camera calibration and distortion parameters (OpenCV) 
Camera.fx: 615.9417724609375
Camera.fy: 616.0935668945312
Camera.cx: 322.3533630371094
Camera.cy: 240.44674682617188

Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.p3: 0.0

Camera.width: 640
Camera.height: 480

# Camera frames per second 
Camera.fps: 30.0

# IR projector baseline times fx (aprox.)
# bf = baseline (in meters) * fx, D435i Of  baseline = 50 mm 
Camera.bf: 30.797   

# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1

# Close/Far threshold. Baseline times.
ThDepth: 40.0

# Deptmap values factor
DepthMapFactor: 1000.0

#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------

# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000

# ORB Extractor: Scale factor between levels in the scale pyramid 
ORBextractor.scaleFactor: 1.2

# ORB Extractor: Number of levels in the scale pyramid 
ORBextractor.nLevels: 8

# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast 
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7

#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize: 2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500