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Plane Filter - Prepare for the FUTURE
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<h1 class="title">
Plane Filter
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<p>
<span class="date">2024/05/17</span>
<br />
<span class="tran-tags">Tags:</span>
<a class="tag is-link is-light" href='tag_Dynamic%20Object%20Removal.html'>#Dynamic Object Removal</a>
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<article class="markdown-body single-content">
<p>本篇文章将对地面点筛选进行进一步研究，以部署到无人机上。</p>
<p>参考代码：<a href="https://github.com/AbangLZU/plane_fit_ground_filter" title="plane_fit_ground_filter">plane_fit_ground_filter</a></p>
<h2><a id="set-up" class="anchor" aria-hidden="true"><span class="octicon octicon-link"></span></a>Set Up</h2>
<p>首先git到本地文件夹：</p>
<pre><code class="language-shell">cd home/ubuntu/yn/SLAM/Dynamic_Object_Removing/my_catkin_ws/src/
git clone https://github.com/AbangLZU/plane_fit_ground_filter.git
cd ..
catkin build plane_ground_filter
source devel/setup.bash
</code></pre>
<p><br />
并且下载好对应的实验数据集存放于<code>/home/ubuntu/yn/SLAM/Dynamic_Object_Removing/Dataset/gournd_filter/kitti_2011_09_26_drive_0005_synced.bag</code></p>
<p>接着，分别开四个终端进行运行：</p>
<pre><code class="language-shell"># terminal-1
roscore
# terminal-2
rosrun rviz rviz
# terminal-3
rosbag play /home/ubuntu/yn/SLAM/Dynamic_Object_Removing/Dataset/gournd_filter/kitti_2011_09_26_drive_0005_synced.bag --loop
# terminal-4 (in "home/ubuntu/yn/SLAM/Dynamic_Object_Removing/my_catkin_ws/")
roslaunch plane_ground_filter plane_ground_filter.launch
</code></pre>
<p><br />
然而发现rviz并没有输出，因此需要对rviz进行改动：</p>
<ul>
<li>在rviz的<code>Global Options</code>中，设置<code>Fixed Frame = base_link</code>；</li>
</ul>
<p><img src="media/17159399785835/2024-05-17%2018-10-52%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE.png" alt="2024-05-17 18-10-52屏幕截图" /></p>
<ul>
<li>在rviz的<code>Display</code>中，添加<code>PointCloud2</code>，并将其中的订阅的topic设为<code>/kitti/velo/pointcloud</code>；</li>
</ul>
<p><img src="media/17159399785835/2024-05-17%2018-11-35%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE.png" alt="2024-05-17 18-11-35屏幕截图" /></p>
<p>此时便可以看到rviz出现了输出：</p>
<p><img src="media/17159399785835/2024-05-17%2018-12-40%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE.png" alt="2024-05-17 18-12-40屏幕截图" /></p>
<p>然而点云并没有具体的颜色信息，发现在进行roslaunch时出现如下问题：</p>
<p><img src="media/17159399785835/2024-05-17%2018-13-26%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE.png" alt="2024-05-17 18-13-26屏幕截图" /></p>
<p>可见，未能正确识别传入点云的<code>intensity</code>和<code>ring</code>信息。</p>
<p>然而经过探索后，发现rviz是可以接收到intensity信息的：当将<code>PointCloud2</code>中的<code>Channel Name</code>改为<code>i</code>后，可以看到此时有了强度信息，而原始的默认情况下，<code>Channel Name = intensity</code>，对比如下：</p>
<p><img src="media/17159399785835/17160968958333.jpg" alt="" /></p>
<p><img src="media/17159399785835/17160971292975.jpg" alt="" /></p>
<p>定位到代码中最开始的ros消息点云格式转换为pcl点云格式的部分：</p>
<pre><code class="language-cpp">// 1.Msg to pointcloud
pcl::PointCloud<VPoint> laserCloudIn;
pcl::fromROSMsg(*in_cloud_ptr, laserCloudIn);
</code></pre>
<p><br />
猜测错误发生在自定义的pcl点云类型<code>VPoint</code>中，找到其定义如下：</p>
<pre><code class="language-cpp">namespace velodyne_pointcloud
{
/** Euclidean Velodyne coordinate, including intensity and ring number. */
struct PointXYZIR
{
PCL_ADD_POINT4D; // quad-word XYZ
float intensity; ///< laser intensity reading
uint16_t ring; ///< laser ring number
EIGEN_MAKE_ALIGNED_OPERATOR_NEW // ensure proper alignment
} EIGEN_ALIGN16;
}; // namespace velodyne_pointcloud
POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_pointcloud::PointXYZIR,
(float, x, x)(float, y, y)(float, z, z)(float, intensity, intensity)(uint16_t, ring, ring))
#define VPoint velodyne_pointcloud::PointXYZIR
</code></pre>
<p><br />
可以看到<code>VPoint</code>即为<code>velodyne_pointcloud::PointXYZIR</code>，是传统的pcl点云类型<code>PCL_ADD_POINT4D</code>与强度<code>intensity</code>和线束<code>ring</code>信息拼接而成。</p>
<p>进而推测是<strong>由于录制的包中，点云强度信息和线束信息的名称与这里的不相符</strong>，因此尝试将强度<code>intensity</code>修改为<code>i</code>：</p>
<pre><code class="language-cpp">namespace velodyne_pointcloud
{
/** Euclidean Velodyne coordinate, including intensity and ring number. */
struct PointXYZIR
{
PCL_ADD_POINT4D; // quad-word XYZ
float i; ///< laser intensity reading
uint16_t ring; ///< laser ring number
EIGEN_MAKE_ALIGNED_OPERATOR_NEW // ensure proper alignment
} EIGEN_ALIGN16;
}; // namespace velodyne_pointcloud
POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_pointcloud::PointXYZIR,
(float, x, x)(float, y, y)(float, z, z)(float, i, i)(uint16_t, ring, ring))
#define VPoint velodyne_pointcloud::PointXYZIR
</code></pre>
<p><br />
此时显示如下：</p>
<p><img src="media/17159399785835/17160996389473.jpg" alt="" /></p>
<p>可以看到，已经解决了无法检测到<code>intensity</code>的问题，接下来尝试将<code>ring</code>改为<code>r</code>：</p>
<pre><code class="language-cpp">namespace velodyne_pointcloud
{
/** Euclidean Velodyne coordinate, including intensity and ring number. */
struct PointXYZIR
{
PCL_ADD_POINT4D; // quad-word XYZ
float i; ///< laser intensity reading
uint16_t r; ///< laser ring number
EIGEN_MAKE_ALIGNED_OPERATOR_NEW // ensure proper alignment
} EIGEN_ALIGN16;
}; // namespace velodyne_pointcloud
POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_pointcloud::PointXYZIR,
(float, x, x)(float, y, y)(float, z, z)(float, i, i)(uint16_t, r, r))
#define VPoint velodyne_pointcloud::PointXYZIR
</code></pre>
<p><br />
然而这样虽然不再显示错误，但是得到的点的<code>r</code>均为0：</p>
<p><img src="media/17159399785835/17161006230274.jpg" alt="" /></p>
<p>然而发现这一切都不是问题：由于我们代码中并没有用到点云的intensity和ring信息，因此只需要观察输出就好。</p>
<p>在rviz中新增两个<code>PointCloud2</code>的展示，分别订阅代码发布的话题<code>/points_no_ground</code>和<code>/points_ground</code>，将其合并输出：</p>
<p><img src="media/17159399785835/17161029760559.jpg" alt="" /></p>
<p>其中我们将地面点标为红色，将非地面点标记为绿色。</p>
<h2><a id="ntu-dataset" class="anchor" aria-hidden="true"><span class="octicon octicon-link"></span></a>NTU Dataset</h2>
<p>在<a href="https://ntu-aris.github.io/ntu_viral_dataset/" title="NTU Dataset">link-1</a>中找到其无人机数据集下载，选择室内数据集<code>nya_01</code>进行处理。下载解压后文件夹格式如下：</p>
<pre><code class="language-shell">├── nya_01
│ │── camera_left.yaml
│ │── camera_right.yaml
│ │── imu_v100.yaml
│ │── leica_prism.yaml
│ │── lidar_horz.yaml
│ │── lidar_vert.yaml
│ │── uwb_nodes.yaml
│ │── nya_01.bag
</code></pre>
<p><br />
通过以下命令播放nya_01包：</p>
<pre><code class="language-shell"># terminal-1
roscore
# terminal-2
rosrun rviz rviz
# terminal-3
rosbag play nya_01.bag --loop
</code></pre>
<p><br />
我们得到相关信息为：</p>
<p><img src="media/17159399785835/17161098622190.jpg" alt="" /></p>
<p>接着添加<code>PointCloud2</code>：令其<code>topic=/os1_cloud_node1/points</code>，然而rviz却报错：<code>Displays/Global Options/Fixed Frame</code>为<code>map</code>时，无法获得相应的数据。</p>
<p>我们采用一下方法得到发布点云的<code>frame_id</code>：</p>
<pre><code class="language-shell">$ rostopic list
/clicked_point
/clock
/dji_sdk/attitude
/dji_sdk/gps_health
/dji_sdk/gps_position
/dji_sdk/height_above_takeoff
/dji_sdk/imu
/imu/imu
/imu/magnetic_field
/imu/temperature
/initialpose
/left/image_raw
/leica/pose/relative
/move_base_simple/goal
/node_pos_marker_sc
/node_pos_sc
/os1_cloud_node1/imu
/os1_cloud_node1/points
/os1_cloud_node2/imu
/os1_cloud_node2/points
/right/image_raw
/rosout
/rosout_agg
/tf
/tf_static
/uwb_endorange_info
/uwb_exorange_info
$ rostopic echo /os1_cloud_node1/points | grep frame_id
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
frame_id: "sensor1/os_sensor"
...
</code></pre>
<p><br />
接着将<code>Displays/Global Options/Fixed Frame</code>改为````sensor1/os_sensor```，此时便得到输出，但是点云数据十分稀疏：</p>
<p><img src="media/17159399785835/17161101788858.jpg" alt="" /></p>
<p>通过改变<code>Display</code>中<code>PointLoud2</code>的<code>size</code>，将其变为<code>0.1m</code>，得到直观点云的俯视图：</p>
<p><img src="media/17159399785835/17161121876561.jpg" alt="" /></p>
<p>然而Outster类型的Lidar的扫描方式和别的不同。</p>
<h2><a id="granary-dataset" class="anchor" aria-hidden="true"><span class="octicon octicon-link"></span></a>Granary Dataset</h2>
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