Skip to content

Latest commit

 

History

History
170 lines (100 loc) · 6.37 KB

README.md

File metadata and controls

170 lines (100 loc) · 6.37 KB
Raw

M-detector

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.

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.

video

1.3 Developers

The codes of this repo are contributed by: Huajie Wu (吴花洁), Yihang Li (李一航) and Wei Xu (徐威)

2. Prerequisites

2.1 Ubuntu and ROS

Ubuntu ≥ 18.04.

ROS ≥ Melodic. Follow [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.

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] (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 git@github.com: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].

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].

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] (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_XXX.launch out_path:="your path for frame-out results" out_origin_path:="your path for point-out results"

Note: Follow the folder structure introduced before, the out_path should be in the format of "(the path to dataset folder)/(dataset name)/sequences/(sequence number)/predictionsx(x is the parameter file's number)/", and the out_origin_path should be in the format of "(the path to dataset folder)/(dataset name)/sequences/(sequence number)/predictionsx_origin(x is the parameter file's number)/".

5.3 Calculate the IoU of results

roslaunch m_detector cal_recall.launch dataset:=(0 for kitti, 1 for nuscenes, 2 for waymo, 3 for avia) dataset_folder:="the path to the dataset_folder" start_se:=(the first sequence number for calculation) end_se:=(the last sequence number for calculation) start_param:=(the first parameter file's number for calculation) end_param:=(the last parameter file's number for calculation) is_origin:=(true for point-out results, false for frame-out results)

Note: Follow the folder structure introduced before, the dataset_folder should be the path to dataset folder. This step will calculate all the IoU for all designated results listed in the dataset folder and generate a new folder named "recall" or "recall_origin" containing the results.

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].

8. License

The source code of this package is released under GPLv2 license. We only allow it free for academic usage. For commercial use, please contact Dr. Fu Zhang fuzhang@hku.hk.

For any technical issues, please contact me via email wu2020@connect.hku.hk.