RGBD数据评测 ORB_SLAM3

环境 ubuntu 16.04 + ROS kinetic + ORB_SLAM

1. 准备数据集以及运行时文件
下载 rgbd_dataset_freiburg2_pioneer_360.tgz 数据， 解压，生成 associations.txt

associate.py文件来自于 /work/ws/src/ORB_SLAM3/evaluation/associate.py， 并稍作修改。

#!/usr/bin/python2.7

# 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])))

执行

PATH_TO_SEQUENCE_FOLDER=/media/iibull/backup/ORB_SLAM3_Data/RGBD/rgbd_dataset
ASSOCIATIONS_FILE=/media/iibull/backup/ORB_SLAM3_Data/RGBD/rgbd_dataset/associations.txt
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM1.yaml ${PATH_TO_SEQUENCE_FOLDER} ${ASSOCIATIONS_FILE}

执行的日志信息为

iibull@URay:/work/ws/src/ORB_SLAM3$ PATH_TO_SEQUENCE_FOLDER=/media/iibull/backup/ORB_SLAM3_Data/RGBD/rgbd_dataset
iibull@URay:/work/ws/src/ORB_SLAM3$ ASSOCIATIONS_FILE=/media/iibull/backup/ORB_SLAM3_Data/RGBD/rgbd_dataset/associations.txt
iibull@URay:/work/ws/src/ORB_SLAM3$ ./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM1.yaml ${PATH_TO_SEQUENCE_FOLDER} ${ASSOCIATIONS_FILE}

ORB-SLAM3 Copyright (C) 2017-2020 Carlos Campos, Richard Elvira, Juan J. Gómez, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
ORB-SLAM2 Copyright (C) 2014-2016 Raúl Mur-Artal, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
This program comes with ABSOLUTELY NO WARRANTY;
This is free software, and you are welcome to redistribute it
under certain conditions. See LICENSE.txt.

Input sensor was set to: RGB-D

Loading ORB Vocabulary. This could take a while...
Vocabulary

Creation of new map with id: 0
Creation of new map with last KF id: 0
Seq. Name:

Camera Parameters:
- Camera: Pinhole
- fx: 517.306
- fy: 516.469
- cx: 318.643
- cy: 255.314
- k1: 0.262383
- k2: -0.953104
- p1: -0.005358
- p2: 0.002628
- k3: 1.16331
- fps: 30
- color order: RGB (ignored if grayscale)

Depth Threshold (Close/Far Points): 0

ORB Extractor Parameters:
- Number of Features: 1000
- Scale Levels: 8
- Scale Factor: 1.2
- Initial Fast Threshold: 20
- Minimum Fast Threshold: 7

-------
Start processing sequence ...
Images in the sequence: 830

First KF:0; Map init KF:0
New Map created with 740 points
virtual int g2o::SparseOptimizer::optimize(int, bool): 0 vertices to optimize, maybe forgot to call initializeOptimization()
virtual int g2o::SparseOptimizer::optimize(int, bool): 0 vertices to optimize, maybe forgot to call initializeOptimization()
virtual int g2o::SparseOptimizer::optimize(int, bool): 0 vertices to optimize, maybe forgot to call initializeOptimization()
KF in map: 77
Fail to track local
Fail to track local
Creation of new map with id: 1
Exits current map
Saved map with ID: 0
Creation of new map with last KF id: 77
First KF:77; Map init KF:77
New Map created with 518 points
Fail to track local
Creation of new map with id: 2
Exits current map
Saved map with ID: 1
Creation of new map with last KF id: 143
First KF:143; Map init KF:143
New Map created with 909 points
virtual int g2o::SparseOptimizer::optimize(int, bool): 0 vertices to optimize, maybe forgot to call initializeOptimization()
-------

median tracking time: 0.0300204
mean tracking time: 0.0314181

Saving camera trajectory to CameraTrajectory.txt ...

Saving keyframe trajectory to KeyFrameTrajectory.txt ...
QObject::~QObject: Timers cannot be stopped from another thread

目前可惜的是： ORB-SLAM3 不是最终产品， 它生成地图但是无法把他们保存到文件中， 也无法从文件加载它们。

目前有 Osmap尝试对ORB-SLAM2的地图（单目地图）进行序列化，它使用了google protoc的协议缓冲。但一直处于有崩溃的情况，在2020.03已经不再更新维持开发计划。