Rename KittiInput to GenericPointInput

Author: AndreasR30

README.md

@@ -39,33 +39,35 @@ Get PDF [here](https://arxiv.org/abs/2311.13976).
 
 ## Acknowledgement
 
-The authors gratefully acknowledge funding by the Federal Office of Bundeswehr Equipment, Information Technology and In-Service Support (BAAINBw).
+The authors gratefully acknowledge funding by the Federal Office of Bundeswehr Equipment, Information Technology and
+In-Service Support (BAAINBw).
 
 ## Overview
+
 - [Examples](#examples)
 - [Run it yourself](#run-it-yourself)
     - [1. Download Sensor Data](#1-download-sensor-data)
-      - [1.1. SemanticKitti](#11-semantickitti)
-      - [1.2. Rosbag of our test vehicle VW Touareg](#12-rosbag-of-our-test-vehicle-vw-touareg)
+        - [1.1. SemanticKitti](#11-semantickitti)
+        - [1.2. Rosbag of our test vehicle VW Touareg](#12-rosbag-of-our-test-vehicle-vw-touareg)
     - [2. Setup Environment](#2-setup-environment)
-      - [2.1. Option: Docker + GUI (VNC)](#21-option-docker--gui-vnc)
-      - [2.2. Option: Locally on Ubuntu 20.04 (Focal) and ROS Noetic](#22-option-locally-on-ubuntu-2004-focal-and-ros-noetic)
+        - [2.1. Option: Docker + GUI (VNC)](#21-option-docker--gui-vnc)
+        - [2.2. Option: Locally on Ubuntu 20.04 (Focal) and ROS Noetic](#22-option-locally-on-ubuntu-2004-focal-and-ros-noetic)
     - [3. Run Continuous Clustering](#3-run-continuous-clustering)
 - [Evaluation on SemanticKITTI Dataset](#evaluation-on-semantickitti-dataset)
-  - [1. Evaluation Results](#1-evaluation-results)
-    - [1.1. Clustering](#11-clustering)
-    - [1.2. Ground Point Segmentation](#12-ground-point-segmentation)
-  - [2. Get Ground Truth Labels](#2-get-ground-truth-labels)
-    - [2.1. Option: Download pre-generated labels](#21-option-download-pre-generated-labels-fastest)
-    - [2.2. Option: Generate with GUI & ROS setup](#22-option-generate-with-gui--ros-setup-assumes-prepared-ros-setup-see-above-useful-for-debugging-etc)
-    - [2.3. Option: Generate without GUI or ROS within Minimal Docker Container](#23-option-generate-without-gui-or-ros-within-minimal-docker-container)
-  - [3. Run Evaluation](#3-run-evaluation)
-    - [3.1. Option: Evaluate with GUI & ROS setup](#31-option-evaluate-with-gui--ros-setup-assumes-prepared-ros-setup-useful-for-debugging)
-    - [3.2. Option: Evaluate without GUI or ROS within Minimal Docker Container](#32-option-evaluate-without-gui-or-ros-within-minimal-docker-container)
+    - [1. Evaluation Results](#1-evaluation-results)
+        - [1.1. Clustering](#11-clustering)
+        - [1.2. Ground Point Segmentation](#12-ground-point-segmentation)
+    - [2. Get Ground Truth Labels](#2-get-ground-truth-labels)
+        - [2.1. Option: Download pre-generated labels](#21-option-download-pre-generated-labels-fastest)
+        - [2.2. Option: Generate with GUI & ROS setup](#22-option-generate-with-gui--ros-setup-assumes-prepared-ros-setup-see-above-useful-for-debugging-etc)
+        - [2.3. Option: Generate without GUI or ROS within Minimal Docker Container](#23-option-generate-without-gui-or-ros-within-minimal-docker-container)
+    - [3. Run Evaluation](#3-run-evaluation)
+        - [3.1. Option: Evaluate with GUI & ROS setup](#31-option-evaluate-with-gui--ros-setup-assumes-prepared-ros-setup-useful-for-debugging)
+        - [3.2. Option: Evaluate without GUI or ROS within Minimal Docker Container](#32-option-evaluate-without-gui-or-ros-within-minimal-docker-container)
 - [Info about our LiDAR Drivers](#info-about-our-lidar-drivers)
-  - [Velodyne](#velodyne)
-  - [Ouster](#ouster)
-- [Tips for Rviz Visualization](#tips-for-rviz-visualization)
+    - [1. Velodyne](#1-velodyne)
+    - [2. Ouster](#2-ouster)
+    - [3. Generic Points](#3-generic-points)
 - [TODOs](#todos)
 
 # Examples:
@@ -88,7 +90,7 @@ filtering potential fog points.
 ## Works on German Highway
 
 There are often no speed limits on the German Highway. So it is not uncommon to see cars with velocities of 180 km/h or
-much higher. A latency of e.g. 200ms leads to positional errors of `(180 / 3.6) m/s * 0.2s = 10m`. This shows the need 
+much higher. A latency of e.g. 200ms leads to positional errors of `(180 / 3.6) m/s * 0.2s = 10m`. This shows the need
 to keep latencies at a minimum.
 
 [![Video GIF](https://user-images.githubusercontent.com/74038190/235294007-de441046-823e-4eff-89bf-d4df52858b65.gif)](https://www.youtube.com/watch?v=DZKuAQBngNE&t=98s)
@@ -134,12 +136,16 @@ our [Google Drive](https://drive.google.com/file/d/1zM4xPRahgxdJXJGHNXYUpM_g4-9U
 environment variable `ROSBAG_PATH` accordingly: `export ROSBAG_PATH=/parent/folder/of/rosbag`
 
 Available bags:
-- `gdown 1zM4xPRahgxdJXJGHNXYUpM_g4-9UrcwC` (3.9GB, [Manual Download](https://drive.google.com/file/d/1zM4xPRahgxdJXJGHNXYUpM_g4-9UrcwC/view?usp=sharing))
-  - Long recording in urban scenario (no camera to reduce file size, no Ouster sensors)
-- `gdown 1qjCG6-nWBZ_2wJwoP80jj0gGopBT2c23` (2.4GB, [Manual Download](https://drive.google.com/file/d/1qjCG6-nWBZ_2wJwoP80jj0gGopBT2c23/view?usp=sharing))
-  - Recording including Ouster 32 sensor data (blurred camera for privacy reasons)
-- `gdown 146IaBdEmkfBWdIgGV5HzrEYDTol84a1H` (0.7GB, [Manual Download](https://drive.google.com/file/d/146IaBdEmkfBWdIgGV5HzrEYDTol84a1H/view?usp=sharing))
-  - Short recording of German Highway (blurred camera for privacy reasons)
+
+- `gdown 1zM4xPRahgxdJXJGHNXYUpM_g4-9UrcwC` (
+  3.9GB, [Manual Download](https://drive.google.com/file/d/1zM4xPRahgxdJXJGHNXYUpM_g4-9UrcwC/view?usp=sharing))
+    - Long recording in urban scenario (no camera to reduce file size, no Ouster sensors)
+- `gdown 1qjCG6-nWBZ_2wJwoP80jj0gGopBT2c23` (
+  2.4GB, [Manual Download](https://drive.google.com/file/d/1qjCG6-nWBZ_2wJwoP80jj0gGopBT2c23/view?usp=sharing))
+    - Recording including Ouster 32 sensor data (blurred camera for privacy reasons)
+- `gdown 146IaBdEmkfBWdIgGV5HzrEYDTol84a1H` (
+  0.7GB, [Manual Download](https://drive.google.com/file/d/146IaBdEmkfBWdIgGV5HzrEYDTol84a1H/view?usp=sharing))
+    - Short recording of German Highway (blurred camera for privacy reasons)
 
 ## 2. Setup Environment
 
@@ -193,12 +199,12 @@ roslaunch continuous_clustering demo_touareg.launch bag_file:=${ROSBAG_PATH}/vw_
 ```
 
 > [!Tip]
-> For the latter launch file, you can use `--wait_for_tf:=false` (default: `true`) argument. It controls whether to wait 
-> for the transform from velodyne to fixed frame (e.g. odometry frame) with a timestamp larger than the one of the 
-> firing or whether to use the latest available (probably incorrect) transform. The former is the accurate approach 
-> (that's why it is the default) but the columns are published in larger batches/slices because they are accumulated 
-> between two transforms. The size of a slice depends on the update rate of the transform (i.e. transforms with 50Hz 
-> lead to batches/slices of 1/5 rotation for a LiDAR rotating with 10Hz). So for a nice visualization where the columns 
+> For the latter launch file, you can use `--wait_for_tf:=false` (default: `true`) argument. It controls whether to wait
+> for the transform from velodyne to fixed frame (e.g. odometry frame) with a timestamp larger than the one of the
+> firing or whether to use the latest available (probably incorrect) transform. The former is the accurate approach
+> (that's why it is the default) but the columns are published in larger batches/slices because they are accumulated
+> between two transforms. The size of a slice depends on the update rate of the transform (i.e. transforms with 50Hz
+> lead to batches/slices of 1/5 rotation for a LiDAR rotating with 10Hz). So for a nice visualization where the columns
 > are published one by one like it the GIF at the top of the page you should disable this flag.
 
 # Evaluation on SemanticKITTI Dataset
@@ -280,8 +286,8 @@ rosrun continuous_clustering gt_label_generator_tool ${KITTI_SEQUENCES_PATH} --n
 ```
 
 > [!TIP]
-> If you want to visualize the generated ground truth labels in an online fashion then remove the `--no-ros` flag and 
-> use just one thread (default). You can then subscribe to the corresponding topic in Rviz and visualize the point 
+> If you want to visualize the generated ground truth labels in an online fashion then remove the `--no-ros` flag and
+> use just one thread (default). You can then subscribe to the corresponding topic in Rviz and visualize the point
 > labels.
 
 ### 2.3. Option: Generate without GUI or ROS within Minimal Docker Container
@@ -326,18 +332,14 @@ docker run --rm -v ${KITTI_SEQUENCES_PATH}:/mnt/kitti_sequences --name build_no_
 docker stop build_no_ros
 ```
 
-# Tips for Rviz Visualization
-
-TODO
-
 # Info about our LiDAR Drivers
 
 Our clustering algorithm is able to process the UDP packets from the LiDAR sensor. So the firings can be processed
 immediately. We directly process the raw UDP packets from the corresponding sensor, which makes the input
 manufacturer/sensor specific. Luckily, we can use external libraries, so it is not necessary reimplement the decoding
 part (UDP Packet -> Euclidean Point Positions). Currently, we support following sensor manufacturers:
 
-## Velodyne
+## 1. Velodyne
 
 - Tested Sensors: VLS 128
 - All other rotating Velodyne LiDARs should work, too
@@ -367,7 +369,7 @@ part (UDP Packet -> Euclidean Point Positions). Currently, we support following
   from [ros-drivers/velodyne](https://github.com/ros-drivers/velodyne) to decode packets to euclidean points
     - See source code at: [velodyne_input.hpp](include/continuous_clustering/ros/velodyne_input.hpp)
 
-## Ouster
+## 2. Ouster
 
 - Tested Sensors: OS 32
 - All other rotating Ouster LiDARs should work, too
@@ -379,7 +381,15 @@ part (UDP Packet -> Euclidean Point Positions). Currently, we support following
   from [ouster-lidar/ouster-ros](https://github.com/ouster-lidar/ouster-ros) to decode packets to euclidean points
     - See source code at: [ouster_input.hpp](include/continuous_clustering/ros/ouster_input.hpp)
 
-## TODOs
+## 3. Generic Points
+
+Alternatively, you can also launch the clustering with parameter `sensor_manufacturer:="generic_points"`. Then it
+subscribes to `<namespace>/raw_data` topic and expects `sensor_msgs::PointCloud2` messages, which should represent one
+firing. It has to be an organized point cloud with `width=1` and `height=<number-of-lasers>`. Required point fields are
+`x`, `y`, `z` (w.r.t. sensor origin), and `intensity`. Missing returns should be represented by `NaN`.
+See [launch/sensor_kitti.launch](launch/sensor_kitti.launch) for such an example.
+
+# TODOs
 
 - Port it to ROS2: planned soon
 - Add features:

include/continuous_clustering/ros/kitti_input.hpp renamed to include/continuous_clustering/ros/generic_points_input.hpp

@@ -1,5 +1,5 @@
-#ifndef CONTINUOUS_CLUSTERING_KITTI_INPUT_HPP
-#define CONTINUOUS_CLUSTERING_KITTI_INPUT_HPP
+#ifndef CONTINUOUS_CLUSTERING_GENERIC_POINTS_INPUT_HPP
+#define CONTINUOUS_CLUSTERING_GENERIC_POINTS_INPUT_HPP
 
 #include <ros/ros.h>
 #include <sensor_msgs/PointCloud2.h>
@@ -10,10 +10,10 @@
 namespace continuous_clustering
 {
 
-class KittiInput : public RosSensorInput<sensor_msgs::PointCloud2>
+class GenericPointsInput : public RosSensorInput<sensor_msgs::PointCloud2>
 {
   public:
-    KittiInput(const ros::NodeHandle& nh, const ros::NodeHandle& nh_private) : RosSensorInput(nh, nh_private)
+    GenericPointsInput(const ros::NodeHandle& nh, const ros::NodeHandle& nh_private) : RosSensorInput(nh, nh_private)
     {
     }
 
@@ -33,21 +33,18 @@ class KittiInput : public RosSensorInput<sensor_msgs::PointCloud2>
         sensor_msgs::PointCloud2ConstIterator<float> iter_y_in(*m, "y");
         sensor_msgs::PointCloud2ConstIterator<float> iter_z_in(*m, "z");
         sensor_msgs::PointCloud2ConstIterator<uint8_t> iter_intensity_in(*m, "intensity");
-        sensor_msgs::PointCloud2ConstIterator<double> iter_kitti_index_in(*m, "original_kitti_index");
 
         int row_index = 0;
         uint64_t stamp = m->header.stamp.toNSec();
-        for (; iter_x_in != iter_x_in.end();
-             ++iter_x_in, ++iter_y_in, ++iter_z_in, ++iter_intensity_in, ++iter_kitti_index_in, ++row_index)
+        for (; iter_x_in != iter_x_in.end(); ++iter_x_in, ++iter_y_in, ++iter_z_in, ++iter_intensity_in, ++row_index)
         {
             current_firing->points[row_index].stamp = stamp;
             current_firing->points[row_index].firing_index = firing_index;
             current_firing->points[row_index].x = *iter_x_in;
             current_firing->points[row_index].y = *iter_y_in;
             current_firing->points[row_index].z = *iter_z_in;
             current_firing->points[row_index].intensity = static_cast<uint8_t>(*iter_intensity_in * 255);
-            current_firing->points[row_index].globally_unique_point_index =
-                *(reinterpret_cast<const uint64_t*>(&(*iter_kitti_index_in))); // PointCloud2 does not support UINT64
+            current_firing->points[row_index].globally_unique_point_index = 0; // only required for evaluation
         }
 
         keepTrackOfMinAndMaxStamp(stamp); // same for firing -> enough to call once

launch/sensor_kitti.launch

@@ -3,7 +3,7 @@
     <arg name="use_rviz" default="true" doc="whether to start rviz"/>
 
     <!-- lidar sensor -->
-    <arg name="sensor_manufacturer" default="kitti"/>
+    <arg name="sensor_manufacturer" default="generic_points"/>
     <arg name="sensor_model" default="kitti"/>
     <arg name="sensor_position" default="roof"/>
     <arg name="sensor_frame" default="velo_link"/>

src/ros/continuous_clustering_node.cpp

@@ -1,5 +1,5 @@
 #include <continuous_clustering/ContinuousClusteringConfig.h>
-#include <continuous_clustering/ros/kitti_input.hpp>
+#include <continuous_clustering/ros/generic_points_input.hpp>
 #include <continuous_clustering/ros/ouster_input.hpp>
 #include <continuous_clustering/ros/ros_transform_synchronizer.hpp>
 #include <continuous_clustering/ros/ros_utils.hpp>
@@ -42,8 +42,8 @@ class RosContinuousClustering
             sensor_input_.reset(new VelodyneInput(nh, nh_private));
         else if (sensor_manufacturer == "ouster")
             sensor_input_.reset(new OusterInput(nh, nh_private));
-        else if (sensor_manufacturer == "kitti")
-            sensor_input_.reset(new KittiInput(nh, nh_private));
+        else if (sensor_manufacturer == "generic_points")
+            sensor_input_.reset(new GenericPointsInput(nh, nh_private));
         else
             throw std::runtime_error("Unknown manufacturer: " + sensor_manufacturer);
         sensor_input_->subscribe();