安装karto slam(带g2o优化）

1.安装g2o

git clone https://github.com/RainerKuemmerle/g2o
cd g2o-master
mkdir build
cd build
cmake ..
make
sudo make install

如果编译没有出错，就会在/usr/local/include看到g2o的包含文件，在usr/local/lib看到g2o的库文件，在usr/local/bin看到g2o的可执行文件。

2.安装Karto

cd catkin_ws/src
git clone https://github.com/ros-perception/open_karto.git
git clone https://github.com/sauravag/slam_karto_g2o
cd ..
catkin_make

catkin_make没有报错的话,基本上就安装成功了.你可以尝试运行,但在运行之前别忘记执行下source devel/setup.bash也可以一劳永逸,在home下的.bashrc末尾追加

source /home/yourusername/catkin_ws/devel/setup.bash

3.下载数据集
之前大家的教程也是,光写文字或者运行,就是不给数据集,搞得我光找数据集就找了半天,功夫不负有心人还真让我给找到了,还不少呢.
戳这里:
SLAM Benchmarking Datasets
下载说明:

进去网站后点你想下载的对应的"download log file"
然后你可能会看到变成页面一堆数据
全部复制,这就是你要的数据集
新建一个文档如data.clf,存之.注意是.clf.
4.处理数据集
这个.clf不是我们用的,我们需要把它转化为特定格式的.bag文件,然后rosbag play来使用数据集,
下面给出转化用的python代码.
先说使用方法:

在你slam_karto下创建一个script文件夹,与launch文件夹同级目录.
把下面代码创建成一个.py文件如convert.py,然后放到script中.
因为他要用到ros库所以必须保存到某个parkage的script中.
cd这个script下,然后python convert.py path/data.clf path/data.bag 转化成功

convert.py

#!/usr/bin/env python
#coding=utf8


'''This is a converter for the Intel Research Lab SLAM dataset
   ( http://kaspar.informatik.uni-freiburg.de/~slamEvaluation/datasets/intel.clf )
   to rosbag'''

import rospy
import rosbag
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from math import pi
from tf2_msgs.msg import TFMessage
from geometry_msgs.msg import TransformStamped
import tf
import sys

def make_tf_msg(x, y, theta, t,base,base0):
    trans = TransformStamped()
    trans.header.stamp = t
    trans.header.frame_id = base
    trans.child_frame_id = base0
    trans.transform.translation.x = x
    trans.transform.translation.y = y
    q = tf.transformations.quaternion_from_euler(0, 0, theta)
    trans.transform.rotation.x = q[0]
    trans.transform.rotation.y = q[1]
    trans.transform.rotation.z = q[2]
    trans.transform.rotation.w = q[3]

    msg = TFMessage()
    msg.transforms.append(trans)
    return msg
if __name__ == "__main__":
    if len(sys.argv) < 3:
        print "请输入dataset文件名。" 
        exit()
    print "正在处理" + sys.argv[1] + "..."
    with open(sys.argv[1]) as dataset:
        with rosbag.Bag(sys.argv[2], 'w') as bag:
            i = 1
            for line in dataset.readlines():
                line = line.strip()
                tokens = line.split(' ')
                if len(tokens) <= 2:
                    continue
                if tokens[0] == 'FLASER':
                    msg = LaserScan()
                    num_scans = int(tokens[1])

                    if num_scans != 180 or len(tokens) < num_scans + 9:
                        rospy.logwarn("unsupported scan format")
                        continue

                    msg.header.frame_id = 'base_laser_link'
                    t = rospy.Time(float(tokens[(num_scans + 8)]))
                    msg.header.stamp = t
                    msg.header.seq = i
                    i += 1
                    msg.angle_min = -90.0 / 180.0 * pi
                    msg.angle_max = 90.0 / 180.0 * pi
                    msg.angle_increment = pi / num_scans
                    msg.time_increment = 0.2 / 360.0
                    msg.scan_time = 0.2
                    msg.range_min = 0.001
                    msg.range_max = 50.0
                    msg.ranges = [float(r) for r in tokens[2:(num_scans + 2)]]
                    msg.ranges.append(float(0))  #我修改了这

                    bag.write('scan', msg, t)

                    odom_x, odom_y, odom_theta = [float(r) for r in tokens[(num_scans + 2):(num_scans + 5)]]
                    tf_msg = make_tf_msg(odom_x, odom_y, odom_theta, t,'odom','base_link')
                    bag.write('tf', tf_msg, t)

                elif tokens[0] == 'ODOM':
                    odom_x, odom_y, odom_theta = [float(t) for t in tokens[1:4]]
                    t = rospy.Time(float(tokens[7]))
                    tf_msg = make_tf_msg(0, 0, 0, t,'base_link','base_laser_link')
                    bag.write('tf', tf_msg, t)

5.测试

roscore

rosrun slam_karto slam_karto

rosbag play data.bag

rosrun rviz rviz

效果：