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58 lines
2.0 KiB
Markdown
58 lines
2.0 KiB
Markdown
# lidar_undistortion_2d
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a ros package for lidar motion compensation
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## Introduction
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读取odom数据对2D激光雷达数据进行运动畸变校正。
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This ros package uses odom transform data to correct motion distortion of a 2D LIDAR in real time。
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## Result
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在图片中，黄色方框代表机器人的位姿，红色点云代表原始的激光雷达数据，白色方框代表经过运动补偿后的激光雷达数据。
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in this picture, the yellow rectangle represents the pose of robot, the red poindcloud represents the origin lidar data, and the white pointcloud represents the lidar data after compensation.
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## Parameters in launch file
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名称 | 类型 | 注释
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-------- | ----- | -----
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scan_sub_topic | string | 订阅的激光数据话题名
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scan_pub_topic | string | 经过运动畸变矫正后发布的激光数据话题名
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enable_pub_pointcloud | bool | 是否将校正后的数据重新封装为LaserScan消息发布
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pointcloud_pub_topic | bool | 经过运动畸变矫正重新封装LaserScan消息话题名
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lidar_frame| string | 激光雷达数据的坐标系
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odom_frame | string | Odometry数据的坐标系
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lidar_scan_time_gain | double | 激光雷达单次扫描时间系数（正常情况下是1.0，但是有些激光雷达的驱动包在计算scan_time时有问题，所以这里乘一个系数）
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## Test with rosbag
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1. compile the project and `source devel/setup.sh`
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2. execute the following command
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```
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roslaunch lidar_undistortion_2d test_lidar_undistortion_2d.launch enable_undistortion:=true
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```
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3. find `/bag/sensor_data.bag`
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```
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rosbag play --clock --pause sensor_data.bag
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```
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remind: '--pause' is essential. otherwise it may lead to error.
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4. result
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the gif showed below represents location with orign lidar data.
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the gif showed below represents location with undistortion lidar data.
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## Reference
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https://github.com/elewu/2d_lidar_undistortion
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深蓝学院SLAM教程
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