evo工具测试TUM数据集中RGB-D序列

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已于 2022-03-06 15:22:58 修改 · 864 阅读

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于 2022-03-03 19:27:35 首次发布

本文指导如何在ORB-SLAM2中安装数据集，进行RGB-D图像关联，并利用evo工具评估轨迹精度。涉及步骤包括解压数据集、使用associate.py匹配图像、运行RGB-D实例和评估轨迹误差。

待补充 EVO安装、数据集下载…

1、解压下载好的数据集

将下载好的数据集解压到ORB-SLAM2/Examples/RGB-D中
在这里插入图片描述

2、进行图像关联

运行RGB-D实例时需要RGBD深度图和RGB图像，所以需要把每一张RGB图像与之对应的RGBD图像建立关联。在associations中有一些自带文件可以直接使用，也可以用关联Python文件associate.py，根据timestamp进行关联。
下面是associate.py文件内容，用法为：在解压后的数据集文件中，新建txt文档，将其复制在txt文档中，修该文件名为associate.py

#!/usr/bin/python
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# 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)
    an