# message_name [message contents] ipc_timestamp ipc_hostname logger_timestamp
# message formats defined: PARAM SYNC ODOM FLASER RLASER TRUEPOS 
# PARAM param_name param_value
# SYNC tagname
# ODOM x y theta tv rv accel
# FLASER num_readings [range_readings] x y theta odom_x odom_y odom_theta
# RLASER num_readings [range_readings] x y theta odom_x odom_y odom_theta
# TRUEPOS true_x true_y true_theta odom_x odom_y odom_theta
# NMEA-GGA utc latitude lat_orient longitude long_orient gps_quality num_sattelites hdop sea_level alitude geo_sea_level geo_sep data_age

自己修改的代码如下

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Software License Agreement (BSD License)
#
# Copyright (c) 2016, Martin Guenther
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above
#    copyright notice, this list of conditions and the following
#    disclaimer in the documentation and/or other materials provided
#    with the distribution.
#  * Neither the name of Willow Garage, Inc. nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.

'''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

def make_tf_msg(x, y, z,theta, t,parentid,childid):
    trans = TransformStamped()
    trans.header.stamp = t
    trans.header.frame_id = parentid
    trans.child_frame_id = childid
    trans.transform.translation.x = x
    trans.transform.translation.y = y
    trans.transform.translation.z = z
    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

with open('aces.clf') as dataset:
    with rosbag.Bag('MIT_Infinite_Corridor_Dataset.bag', 'w') as bag:
        for line in dataset.readlines():
            line = line.strip()
            tokens = line.split(' ')
            if len(tokens) <= 2:
                continue
            if tokens[0] == 'FLASER':  #FLASER这是原始文件中原来标注激光数据的，一般不需要更改
                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'  #这是激光坐标系id，根据需要更改，在ROS中读取数据时会使用到，一般也不需要更改。
                t = rospy.Time(float(tokens[(num_scans + 8)])) #获取时间
                msg.header.stamp = t
                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)]]

                bag.write('scan', msg, t)  #这是激光数据发布的话题，根据节点程序订阅的具体话题更改。

                odom_x, odom_y, odom_theta = [float(r) for r in tokens[(num_scans + 2):(num_scans + 5)]]
                tf_msg = make_tf_msg(0.1, 0, 0.2, 0, t,'base_link','base_laser')
                bag.write('tf', tf_msg, t)

            elif tokens[0] == 'ODOM':  #标志里程计数据
                odom_x, odom_y, odom_theta = [float(t) for t in tokens[1:4]]
                t = rospy.Time(float(tokens[7]))
                msg=Odometry()

                msg.header.stamp=t
                msg.header.frame_id='odom'
                msg.child_frame_id='base_link'
                msg.pose.pose.position.x=odom_x
                msg.pose.pose.position.y=odom_y
                msg.pose.pose.position.z=0
                q = tf.transformations.quaternion_from_euler(0, 0, odom_theta)
                msg.pose.pose.orientation.x = q[0]
                msg.pose.pose.orientation.y = q[1]
                msg.pose.pose.orientation.z = q[2]
                msg.pose.pose.orientation.w = q[3]
                bag.write('odom', msg, t) 

                # tf_msg = make_tf_msg(odom_x, odom_y,0 , odom_theta, t,'odom','base_link')
                # bag.write('tf', tf_msg, t)

GitHub - Robots90/clf2bag: Transfer clf file to bag file for ROS

.log .clf文件转化为 .bag文件 github_Robots.的博客-CSDN博客

用数据集跑激光slam,gmapping/karto_范坤3371的博客-CSDN博客

参考下面图片对corrected-log的数据进行进一步处理

获取odom数据

获取lidar数据可以参考：

carmen_to_rosbag/old_laser_scan.py at master · art32fil/carmen_to_rosbag · GitHub

最终修改版本如下：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
 
# Software License Agreement (BSD License)
#
# Copyright (c) 2016, Martin Guenther
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above
#    copyright notice, this list of conditions and the following
#    disclaimer in the documentation and/or other materials provided
#    with the distribution.
#  * Neither the name of Willow Garage, Inc. nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
 
'''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
 
def make_tf_msg(x, y, z,theta, t,parentid,childid):
    trans = TransformStamped()
    trans.header.stamp = t
    trans.header.frame_id = parentid
    trans.child_frame_id = childid
    trans.transform.translation.x = x
    trans.transform.translation.y = y
    trans.transform.translation.z = z
    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
 
 #    0   |      1       | 2 - 1+num_readings | 2+num_readings | 3+num_readings |
# xLASER | num_readings |  [range_readings]  |       x        |        y       |
#-------------------------------------------------------------------------------
# 4+num_readings | 5+num_readings | 6+num_readings | 7+num_readings |
#      theta     |     odom_x     |     odom_y     |   odom_theta   |
#-------------------------------------------------------------------------------
# 8+num_readings | 9+num_readings | 10+num_readings
# ipc_timestamp | ipc_hostname  |logger_timestamp

with open('mit-killian.clf') as dataset:
    with rosbag.Bag('MIT.bag', 'w') as bag:
        for line in dataset.readlines():
            line = line.strip()
            tokens = line.split(' ')
            if len(tokens) <= 2:
                continue
            if tokens[0] == 'FLASER':  #FLASER这是原始文件中原来标注激光数据的，一般不需要更改
                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'  #这是激光坐标系id，根据需要更改，在ROS中读取数据时会使用到，一般也不需要更改。
                t = rospy.Time(float(tokens[(num_scans + 8)])) #获取时间
                msg.header.stamp = t
                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)]]
 
                bag.write('scan', msg, t)  #这是激光数据发布的话题，根据节点程序订阅的具体话题更改。
 
                tf_msg = make_tf_msg(0.1, 0, 0.2, 0, t,'base_link','base_laser')
                bag.write('tf', tf_msg, t)

                # odom_x, odom_y, odom_theta = [float(r) for r in tokens[(num_scans + 2):(num_scans + 5)]] 
                # odom_x, odom_y, odom_theta = [float(r) for r in tokens[(num_scans + 5):(num_scans + 8)]]
                # msg1=Odometry()
                # msg1.header.stamp=t
                # msg1.header.frame_id='odom'
                # msg1.child_frame_id='base_link'
                # msg1.pose.pose.position.x=odom_x
                # msg1.pose.pose.position.y=odom_y
                # msg1.pose.pose.position.z=0
                # q = tf.transformations.quaternion_from_euler(0, 0, odom_theta)
                # msg1.pose.pose.orientation.x = q[0]
                # msg1.pose.pose.orientation.y = q[1]
                # msg1.pose.pose.orientation.z = q[2]
                # msg1.pose.pose.orientation.w = q[3]
                # bag.write('odom', msg1, t) 


#    0 | 1 | 2 |  3  | 4 | 5 |  6  |      7      |      8     |        9
#  ODOM| x | y |theta| tv| rv|accel|ipc_timestamp|ipc_hostname|logger_timestamp 
            elif tokens[0] == 'ODOM':  #标志里程计数据
                odom_x, odom_y, odom_theta = [float(t) for t in tokens[1:4]]
                t = rospy.Time(float(tokens[7]))
                msg=Odometry()
                msg.header.stamp=t
                msg.header.frame_id='odom'
                msg.child_frame_id='base_link'
                msg.pose.pose.position.x=odom_x
                msg.pose.pose.position.y=odom_y
                msg.pose.pose.position.z=0
                q = tf.transformations.quaternion_from_euler(0, 0, odom_theta)
                msg.pose.pose.orientation.x = q[0]
                msg.pose.pose.orientation.y = q[1]
                msg.pose.pose.orientation.z = q[2]
                msg.pose.pose.orientation.w = q[3]
                bag.write('odom', msg, t) 
 
                # tf_msg = make_tf_msg(odom_x, odom_y,0 , odom_theta, t,'odom','base_link')
                # bag.write('tf', tf_msg, t)

跑MIT Infinite Corridor Dataset的效果如下：

所用的参数文档如下

-- Copyright 2016 The Cartographer Authors
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--      http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.

include "map_builder.lua"
include "trajectory_builder.lua"

options = 
  map_builder = MAP_BUILDER,                -- map_builder.lua的配置信息
  trajectory_builder = TRAJECTORY_BUILDER,  -- trajectory_builder.lua的配置信息
  
  map_frame = "map",                        -- 地图坐标系的名字
  tracking_frame = "base_laser",              -- 将所有传感器数据转换到这个坐标系下
  published_frame = "base_link",            -- tf: map -> published_frame
  odom_frame = "odom",                      -- 里程计的坐标系名字
  provide_odom_frame = true,               -- 是否提供odom的tf, 如果为true,则tf树为map->odom->published_frame
                                            -- 如果为false tf树为map->published_frame

  publish_frame_projected_to_2d = false,    -- 是否将坐标系投影到平面上
  use_pose_extrapolator = true,            -- 发布tf时是使用pose_extrapolator的位姿还是前端计算出来的位姿

  use_odometry = true,                     -- 是否使用里程计,如果使用要求一定要有odom的tf
  use_nav_sat = false,                      -- 是否使用gps
  use_landmarks = false,                    -- 是否使用landmark
  num_laser_scans = 1,                      -- 是否使用单线激光数据
  num_multi_echo_laser_scans = 0,           -- 是否使用multi_echo_laser_scans数据
  num_subdivisions_per_laser_scan = 1,      -- 1帧数据被分成几次处理,一般为1
  num_point_clouds = 0,                     -- 是否使用点云数据
  
  lookup_transform_timeout_sec = 0.2,       -- 查找tf时的超时时间
  submap_publish_period_sec = 0.3,          -- 发布数据的时间间隔
  pose_publish_period_sec = 5e-3,
  trajectory_publish_period_sec = 30e-3,

  rangefinder_sampling_ratio = 1.,          -- 传感器数据的采样频率
  odometry_sampling_ratio = 1.,
  fixed_frame_pose_sampling_ratio = 1.,
  imu_sampling_ratio = 1.,
  landmarks_sampling_ratio = 1.,


MAP_BUILDER.use_trajectory_builder_2d = true

TRAJECTORY_BUILDER_2D.min_range = 0.001
TRAJECTORY_BUILDER_2D.max_range = 100.
TRAJECTORY_BUILDER_2D.submaps.num_range_data = 35

TRAJECTORY_BUILDER_2D.missing_data_ray_length = 1.
TRAJECTORY_BUILDER_2D.use_imu_data = false  --不采用imu数据
TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching = true
TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.linear_search_window = 0.1
TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.translation_delta_cost_weight = 10.
TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.rotation_delta_cost_weight = 1e-1

POSE_GRAPH.optimization_problem.huber_scale = 1e2
POSE_GRAPH.optimize_every_n_nodes = 35
POSE_GRAPH.constraint_builder.min_score = 0.65

-- -- ####################################################################################################
-- MAP_BUILDER.use_trajectory_builder_2d = true
-- MAP_BUILDER.num_background_threads =6  --建议优化至每个机器的CPU线程数量
 
-- TRAJECTORY_BUILDER_2D.num_accumulated_range_data = 1 --积累几帧激光数据作为一个标准单位scan
-- TRAJECTORY_BUILDER_2D.min_range = 0.01  --激光的最近有效距离
-- TRAJECTORY_BUILDER_2D.max_range = 26.   --激光最远的有效距离（可以接受的调整参数范围在30 - 15之间，推荐20-25误差较小。）
-- -- TRAJECTORY_BUILDER_2D.missing_data_ray_length = 5. --无效激光数据设置距离为该数值
-- TRAJECTORY_BUILDER_2D.use_imu_data = false  --是否使用imu数据
-- TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching = true
-- TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.linear_search_window = 0.1 
-- --线距离搜索框，在这个框的大小内，搜索最佳scan匹配  减小该参数可以增强实时的建图效果，降低闭环优化的效果，形成闭环时，产生的重影较多
-- TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher. angular_search_window = math.rad(10.) --角度搜索框的大小
-- TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.translation_delta_cost_weight = 20.
-- TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.rotation_delta_cost_weight = 2e-1  --影响的是过程中的效果，间接会影响最后的优化时间长
-- TRAJECTORY_BUILDER_2D.ceres_scan_matcher.translation_weight = 30.
-- TRAJECTORY_BUILDER_2D.ceres_scan_matcher.rotation_weight = 30.
-- TRAJECTORY_BUILDER_2D.ceres_scan_matcher.ceres_solver_options.max_num_iterations = 7  --该参数误差波动较大，平均误差大时最大误差小，平均误差小时最大误差大，在7左右处于相对平衡点，推荐7-10为修改参数范围
-- TRAJECTORY_BUILDER_2D.submaps.num_range_data = 80 --num_range_data设置的值与CPU有这样一种关系，值小(10)，CPU使用率比较稳定，整体偏高，值大时，CPU短暂爆发使用(插入子图的时候)，平时使用率低，呈现极大的波动状态。 可以接受的调整参数范围在90 - 60之间，在80左右效果较为优秀。
-- TRAJECTORY_BUILDER_2D.submaps.range_data_inserter.probability_grid_range_data_inserter.hit_probability = 0.55
-- TRAJECTORY_BUILDER_2D.submaps.range_data_inserter.probability_grid_range_data_inserter.miss_probability = 0.49
-- TRAJECTORY_BUILDER_2D.motion_filter.max_distance_meters = 0.05   //尽量小点  // 如果移动距离过小, 或者时间过短, 不进行地图的更新
-- TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians = math.rad(0.3)
-- TRAJECTORY_BUILDER_2D.motion_filter.max_time_seconds = 0.5

-- -- 增加如下
-- TRAJECTORY_BUILDER_2D.voxel_filter_size=0.1 --平均误差在0.1 和0.2时发生起伏，虽然0.1的误差比0.2的更大，但由于0.2时绘图偏差严重，建议选择小于0.1的值。
-- TRAJECTORY_BUILDER_2D.submaps.resolution=0.05 --平均误差在0.1时达到顶峰，建议小于0.1较为良好。
-- TRAJECTORY_BUILDER_2D.adaptive_voxel_filter.max_range=30 --可以接受的调整参数范围在50 - 20之间，在30左右效果较为优秀。
-- TRAJECTORY_BUILDER_2D.adaptive_voxel_filter.max_length=4 --可以接受的调整参数范围在0.5 - 4之间，在2 - 4左右效果较为优秀。
-- TRAJECTORY_BUILDER_2D.adaptive_voxel_filter.min_num_points=200 --可以接受的调整参数范围在100 - 200之间，100以上的效果较为优秀。
 

-- POSE_GRAPH.optimization_problem.huber_scale = 1e2  --鲁棒核函数，去噪
-- POSE_GRAPH.optimize_every_n_nodes = 35   --后端优化节点 （推荐参数修改范围为30-70，官方文档中建议减少该值来提高速度，推荐30左右配置较为良好）
-- POSE_GRAPH.global_constraint_search_after_n_seconds = 30   --该参数随着间隔时间的增加误差的方差和最大误差逐渐下降，但最大误差逐渐上升，在30-40时处于两者的平衡点。推荐30-40为修改参数范围
-- POSE_GRAPH.optimization_problem.ceres_solver_options.max_num_iterations = 15  --优化迭代步数
-- POSE_GRAPH.optimization_problem.ceres_solver_options.num_threads = 1
-- POSE_GRAPH.constraint_builder.max_constraint_distance = 15.
-- POSE_GRAPH.constraint_builder.sampling_ratio = 0.25   --该参数在0.2-0.25左右平均误差处于相对低位。最小方差的位置在在0.25左右，推荐参数修改范围为0.2-0.3
-- POSE_GRAPH.constraint_builder.min_score = 0.75  --该参数在0.75-0.95误差的方差和最大误差处于相对低位，推荐0.75-0.95为修改参数范围
-- POSE_GRAPH.constraint_builder.global_localization_min_score = 0.6
-- POSE_GRAPH.constraint_builder.fast_correlative_scan_matcher.linear_search_window = 3.
-- POSE_GRAPH.constraint_builder.fast_correlative_scan_matcher.branch_and_bound_depth = 5. --搜索方法，界定分支法，求解问题构成一个搜索树，depth是构造树的深度
-- POSE_GRAPH.global_sampling_ratio = 0.001  --在0.001，0.002时平均误差处于较小位置，优化时可以考虑的值为0.001和0.002
-- -- 增加如下


return options

参考资料

常用的公共数据集（一）_yhgao96的博客-CSDN博客_公共数据集

CVonline: Image Databases

OpenLORIS-Scene Datasets

以上是关于学习笔记之——数据集的生成的主要内容，如果未能解决你的问题，请参考以下文章

实验笔记之——CARMEN （.log .clf）文件转换为rosbag

ROS实验笔记之——UZH-FPV数据集的验证

《南溪的目标检测学习笔记》——COCO数据集的学习笔记

ROS实验笔记之——UZH-FPV数据集的验证