CRLFnet

The source code of the CRLFnet.

INSTALL & BUILD

Env: Ubuntu20.04 + ROS(Noetic) + Python3.x

• If using Google-colab, there is a recommanded environment: CUDA10.2+PyTorch1.6.
• Refer to INSTALL.md for the installation of OpenPCDet.
• Install ros_numpy package mannually: [Source code][Install]

Absolute paths may need your mind:

file path	Line(s)
src/camera_info/get_cam_info.cpp	26,64,102,140,178,216,254,292,330,368,
src/LidCamFusion/OpenPCDet/tools/cfgs/custom_models/pointrcnn.yaml	4,5
src/LidCamFusion/OpenPCDet/tools/cfgs/custom_models/pv_rcnn.yaml	5,6

Docker

Build project from Dockerfile:

docker build -t [name]:tag /docker/

or pull image directly:

docker pull gzzyyxy/crlfnet:yxy

Launch the Site

This needs ROS to be installed.

    cd /ROOT
    
    # launch the site
    roslaunch site_model spwan.launch
    
    # launch the vehicles (optional)
    woslaunch pkg racecar.launch

Rad-Cam Fusion

This part integrates the Kalman-Filter to real-time radar data.

Necessary Configurations on GPU and model data

• Set use_cuda to True in src/site_model/config/config.yaml to use GPU.

• Download yolo_weights.pth from jbox, and move to src/site_model/src/utils/yolo/model_data.

Run The Rad-Cam Fusion Model

The steps to run the radar-camera fusion is listed as follows.

For the last command, an optional parameter --save or -s is available if you need to save the track of vehicles as images. The --mode or -m parameter has three options, which are normal, off-yolo and from-save. The off-yolo and from-save modes enable the user to run YOLO seprately to simulate a higher FPS.

    #--- AFTER THE SITE LAUNCHED ---#
    # run the radar message filter
    rosrun site_model radar_listener.py
    
    # run the rad-cam fusion program
    cd src/site_model
    python -m src.RadCamFusion.fusion [-m MODE] [-s]

Camera Calibration

The calibration parameters are needed in related camera-data transformation. Once the physical models are modified, update the camera calibration parameters:

    #--- AFTER THE SITE LAUNCHED ---#
    # get physical parameters of cameras
    rosrun site_model get_cam_info

    # generate calibration formula according to parameters of cameras
    python src/site_model/src/utils/generate_calib.py

Lid-Cam Fusion

This part integrates OpenPCDet to real-time lidar object detection, refer to CustomDataset.md to find how to proceed with self-product dataset using only raw lidar data.

Config Files

Configurations for model and dataset need to be specified:

• Model Configs tools/cfgs/custom_models/XXX.yaml
• Dataset Configs tools/cfgs/dataset_configs/custom_dataset.yaml

Now pointrcnn.yaml and pv_rcnn.yaml are supported.

Datasets

Create dataset infos before training:

    cd OpenPCDet/
    python -m pcdet.datasets.custom.custom_dataset create_custom_infos tools/cfgs/dataset_configs/custom_dataset.yaml

File custom_infos_train.pkl, custom_dbinfos_train.pkl and custom_infos_test.pkl will be saved to data/custom.

Train

Specify the model using YAML files defined above.

    cd tools/
    python train.py --cfg_file path/to/config/file/

For example, if using PV_RCNN for training:

    cd tools/
    python train.py --cfg_file cfgs/custom_models/pv_rcnn.yaml --batch_size 2 --workers 4 --epochs 80

Pretrained Model

Download pretrained model through these links:

model	time cost	URL
PointRCNN	~3h	Google drive / Jbox
PV_RCNN	~6h	Google drive / Jbox

Predict (Local)

Prediction on local dataset help to check the result of trainin. Prepare the input properly.

python pred.py --cfg_file path/to/config/file/ --ckpt path/to/checkpoint/ --data_path path/to/dataset/

For example:

python pred.py --cfg_file cfgs/custom_models/pv_rcnn.yaml --ckpt ../output/custom_models/pv_rcnn/default/ckpt/checkpoint_epoch_80.pth --data_path ../data/custom/testing/velodyne/

Visualize the results in rviz, white boxes represents the vehicles.

Lid-Cam Fusion

Follow these steps for only lidar-camera fusion. Some of them need different bash terminals. For the last command, additional parameter --save_result is required if need to save the results of fusion in the form of image.

    #--- AFTER THE SITE LAUNCHED --#
    # cameras around lidars start working
    python src/site_model/src/LidCamFusion/camera_listener.py 

    # lidars start working
    python src/site_model/src/LidCamFusion/pointcloud_listener.py

    # combine all the point clouds and fix their coords
    rosrun site_model pointcloud_combiner

    # start camera-lidar fusion
    cd src/site_model/
    python -m src.LidCamFusion.fusion [--config] [--eval] [--re] [--disp] [--printl] [--printm]

TODO...

Issues

Some problems may occurred during debugging.

• Confused: set the batch_size=1 and still out of memory: open-mmlab/OpenPCDet#140
• 段错误(核心已转储) when run dem.py: open-mmlab/OpenPCDet#846
• N > 0 assert faild. CUDA kernel launch blocks must be positive, but got N= 0 when training: open-mmlab/OpenPCDet#945
• raise NotImplementedError, NaN or Inf found in input tensor when training: open-mmlab/OpenPCDet#280
• fix recall calculation bug for empty scene: open-mmlab/OpenPCDet#908
• installation Error " fatal error: THC/THC.h: No such file or directory #include <THC/THC.h> ": open-mmlab/OpenPCDet#1014
• ...
• Welcome to report more issues!

Star History

## Star History