first version for open source

CMakeLists.txt

@@ -0,0 +1,106 @@
+cmake_minimum_required(VERSION 3.1.0)
+project(m_detector)
+
+SET(CMAKE_BUILD_TYPE "Release")
+
+ADD_COMPILE_OPTIONS(-std=c++17 )
+set( CMAKE_CXX_FLAGS "-std=c++17 -O3 -ltbb" ) 
+
+add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\")
+
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" )
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CXX_EXTENSIONS OFF)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17 -ltbb -pthread -std=c++1z -std=c++17 -fexceptions")
+
+message("Current CPU archtecture: ${CMAKE_SYSTEM_PROCESSOR}")
+if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" )
+  include(ProcessorCount)
+  ProcessorCount(N)
+  message("Processer number:  ${N}")
+  # if(N GREATER 15)
+  #   add_definitions(-DMP_EN)
+  #   add_definitions(-DMP_PROC_NUM=14)
+  #   message("core for MP:  4")
+  # elseif(N GREATER 5)
+  if(N GREATER 5)
+    add_definitions(-DMP_EN)
+    add_definitions(-DMP_PROC_NUM=4)
+    message("core for MP:  3")
+  elseif(N GREATER 3)
+    math(EXPR PROC_NUM "${N} - 2")
+    add_definitions(-DMP_EN)
+    add_definitions(-DMP_PROC_NUM="${PROC_NUM}")
+    message("core for MP:  ${PROC_NUM}")
+  else()
+    add_definitions(-DMP_PROC_NUM=1)
+  endif()
+else()
+  add_definitions(-DMP_PROC_NUM=1)
+endif()
+
+find_package(OpenMP QUIET)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}   ${OpenMP_C_FLAGS}")
+
+# find_package(PythonLibs REQUIRED)
+# find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h")
+
+set(TBB_DIR "/usr/local/tbb-2019_U8")
+include("/home/huajie/package/oneTBB-2019_U8/cmake/TBBBuild.cmake")
+tbb_build(TBB_ROOT "/home/huajie/package/oneTBB-2019_U8" CONFIG_DIR TBB_DIR)
+find_package(catkin REQUIRED COMPONENTS
+  geometry_msgs
+  nav_msgs
+  sensor_msgs
+  roscpp
+  rospy
+  std_msgs
+  pcl_ros
+  tf
+  livox_ros_driver
+  message_generation
+  eigen_conversions
+)
+
+find_package(Eigen3 REQUIRED)
+find_package(PCL 1.8 REQUIRED)
+find_package(TBB REQUIRED)
+find_package(OpenCV REQUIRED)
+
+message(Eigen: ${EIGEN3_INCLUDE_DIR})
+
+include_directories(
+	${catkin_INCLUDE_DIRS} 
+  ${EIGEN3_INCLUDE_DIR}
+  ${PCL_INCLUDE_DIRS}
+  ${PYTHON_INCLUDE_DIRS}
+  include)
+
+generate_messages(
+ DEPENDENCIES
+ geometry_msgs
+)
+
+catkin_package(
+  CATKIN_DEPENDS geometry_msgs nav_msgs roscpp rospy std_msgs message_runtime
+  DEPENDS EIGEN3 PCL
+  INCLUDE_DIRS
+)
+
+
+add_executable(dynfilter src/dynfilter_with_odom.cpp src/DynObjFilter.cpp src/DynObjCluster.cpp)
+target_link_libraries(dynfilter ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES} ${OpenCV_LIBS} TBB::tbb)
+target_include_directories(dynfilter PRIVATE ${PYTHON_INCLUDE_DIRS})
+
+add_executable(display_prediction src/display_prediction.cpp )
+target_link_libraries(display_prediction ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES} ${OpenCV_LIBS} TBB::tbb)
+target_include_directories(display_prediction PRIVATE ${PYTHON_INCLUDE_DIRS})
+
+
+add_executable(cal_recall src/cal_recall_multi.cpp)
+target_link_libraries(cal_recall ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES} ${OpenCV_LIBS} TBB::tbb)
+target_include_directories(cal_recall PRIVATE ${PYTHON_INCLUDE_DIRS})
+
+

README.md

@@ -1,3 +1,166 @@
 # M-detector
 
-### Moving Event Detection from LiDAR Point Streams
+## 1.Introduction
+
+**M-detector** is a moving event detection package, which determines if a point from LiDAR is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors.
+
+### **1.1 Related paper**
+
+Our related papers has been accepted by Nature Communications. [Moving Event Detection from LiDAR Stream Points](https://www.nature.com/articles/s41467-023-44554-8).
+
+If our code is used in your project, please cite our paper.
+
+### **1.2 Related video**
+
+Our accompanying videos are now available on **YouTube** (click below images to open) and [**Bilibili**](xxx).
+
+<div align="center">
+<a href="https://www.youtube.com/watch?v=SYaig2eHV5I" target="_blank"><img src="img/cover.bmp" alt="video" width="60%" /></a>
+</div>
+
+### 1.3 Developers
+
+The codes of this repo are contributed by:
+[Huajie Wu (吴花洁)](https://github.com/HuajieWu99), [Yihang Li (李一航)](https://github.com/yihangHKU) and [Wei Xu (徐威)](https://github.com/XW-HKU)
+
+## 2. Prerequisites
+
+### 2.1 **Ubuntu** and **ROS**
+
+Ubuntu ≥ 18.04.
+
+ROS     ≥ Melodic. Follow [[ROS Installation](http://wiki.ros.org/ROS/Installation)]
+
+### 2.2 **PCL** and **Eigen**
+
+PCL      ≥ 1.8
+
+`sudo apt install libpcl-dev`
+
+Eigen    ≥ 3.3.4
+
+`sudo apt install libeigen3-dev`
+
+### 2.3 **livox_ros_driver**
+
+Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
+
+*Remarks:*
+
+* Since the M-detector support Livox serials LiDAR firstly, so the **livox_ros_driver** must be installed and **sourced** before run any M-detector launch file.
+* How to source? The easiest way is add the line `source $Livox_ros_driver_dir$/devel/setup.bash` to the end of file `~/.bashrc`, where `$Livox_ros_driver_dir$` is the directory of the livox ros driver workspace (should be the `ws_livox` directory if you completely followed the livox official document).
+
+### 2.4 TBB
+
+Install gcc-9 g++-9
+
+`sudo add-apt-repository ppa:ubuntu-toolchain-r/test`
+
+`sudo apt update`
+
+`sudo apt install gcc-9 g++-9`
+
+`cd /usr/bin`
+
+`sudo rm gcc g++`
+
+`sudo ln -s gcc-9  gcc`
+
+`sudo ln -s g++-9 g++`
+
+Follow [[TBB Installation](https://solarianprogrammer.com/2019/05/09/cpp-17-stl-parallel-algorithms-gcc-intel-tbb-linux-macos/)] (**Note:** change the gcc-9.1/g++-9.1 to gcc-9/g++-9)
+
+## 3. Build
+
+Clone the repository and catkin_make:
+
+`cd ~/catkin_ws/src`
+
+`git clone [email protected]:hku-mars/M-detector.git`
+
+`catkin_make`
+
+`source devel/setup.bash`
+(**Note:** change the path for TBB in CMakeList.txt)
+
+## 4. Key Information
+
+### 4.1 Key parameters
+
+```
+dataset: 3    #0 for kitti, 1 for nuscenes, 2 for waymo
+buffer_delay: 0.1
+buffer_size: 100000
+points_num_perframe: 30000
+depth_map_dur: 0.2
+max_depth_map_num: 5
+max_pixel_points: 5
+frame_dur: 0.1
+hor_resolution_max: 0.005
+ver_resolution_max: 0.01
+```
+
+The parameters are provided in folder "config" for different LiDARs.
+
+For methods of parameters tuning, please follow the section 8 introduced in [[Supplementary Information](https://www.nature.com/articles/s41467-023-44554-8)].
+
+To save the label files, please pass the parameter via the corresponding launch files.
+
+### 4.2 Folder structure for dataset
+
+```
+├── XXX (dataset name)
+│   ├── bags
+│   │   ├── XXX_0000.bag
+│   │   ├── ...
+│   ├── sequences
+│   │   ├── 0000
+│   │   │   ├── labels
+│   │   │   ├── predictionsx_origin (results in point-out mode with xth parameter file)
+│   │   │   ├── predictionsx (in frame-out mode with xth parameter file)
+│   │   │   ├── ...
+│   │   ├── ...
+├── ...
+```
+
+The dataset can be downloaded at [[this link](https://drive.google.com/drive/folders/1ASNfrjZB7n9Q-nB4Pm2IwvArFWnTcFAj?usp=drive_link)].
+
+## 5. Directly Run
+
+### 5.1 Run with odometry and point clouds (in local frame)
+
+At first, please run a odometery node, such as [[Fast Lio](https://github.com/hku-mars/FAST_LIO)] (Download Fast Lio provided in Releases into the same location as M-detector's and complie them).
+
+Then:
+
+`roslaunch fast_lio mapping_XXX(for dataset).launch`
+
+`roslaunch m_detector detector_(dataset).launch`
+
+`rosbag play YOURBAG.bag`
+
+### 5.2 Generate the label files for every point
+
+`roslaunch m_detector detector_odom.launch out_path:="your path for frame-out results" out_origin_path:="your path for point-out results"`
+
+### 5.3 Calculate the IoU of results
+
+`roslaunch m_detector cal_recall.launch`
+
+## 6. Run with Embedded in FAST LIO
+
+Download the embedded version provided in Releases into a new workspace and complie them.
+
+`roslaunch fast_lio mapping_(dataset).launch`
+
+`rosbag play YOURBAG.bag`
+
+## 7. Rosbag Download
+
+The bags used in paper can be download at [[this link](https://drive.google.com/drive/folders/1ASNfrjZB7n9Q-nB4Pm2IwvArFWnTcFAj?usp=sharing)].
+
+## 8. License
+
+The source code of this package is released under [**GPLv2**](http://www.gnu.org/licenses/) license. We only allow it free for **academic usage**. For commercial use, please contact Dr. Fu Zhang [[email protected]](mailto:[email protected]).
+
+For any technical issues, please contact me via email [[email protected]](mailto:[email protected]).