Skip to content

The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection

License

Notifications You must be signed in to change notification settings

VirtualRoyalty/PointCloudSegmentation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PointCloudSegmentation


CI simple_checks



Project structure:

├───docker-env/
├───obstacle-detection/
│   ├───dataset/
│   │   └───sequences/
│   │       └───00/
│   │           ├───clusters/
│   │           ├───labels/
│   │           └───velodyne/
|   ├───model/
|   |
│   ├───examples/
│   │   
│   ├───pipeline/
│   │  
│   └───scripts/
│       
└───visualization/

How to dockerize this:


  • In base-notebook/ folder start Docker and build an image: $ docker build -t jupyter .
  • After that you can verify a successful build by running: $ docker images
  • Then start container by running:

    $ docker run -it --rm -p 8888:8888 -v /path/to/obstacle-detection:/home/jovyan/work jupyter

    NOTE: on Windows you need to convert your path into a quasi-Linux format (e.g. //c/path/to/obstacle-detection). More details here
    Also, if you want to use drive D:/ you need to check whether it is mounted or not and if not mount it manually. More details here if you use Docker toolbox

  • After correct running you will see URL to access jupyter, e.g.:

    httр://127.0.0.1:8888?token=0cccd15e74216ed2dbe681738ed0f9c78bf65515e94f27a8

  • To access jupyter you need to go for Docker IP:8888?token=xxxx...
    ( e.g. httр://192.168.99.100:8888/?token=0cccd15e74216ed2dbe681738ed0f9c78bf65515e94f27a8)

  • To enter a docker container run $ docker exec -it *CONTAINER ID* bash (find out ID by running $ docker ps)

Pre-trained Models

References and useful links:



Dataset:

  1. Web-site Semantic KITTI
  2. Paper Semantic KITTI


Segmentation:

  1. Segmentation approaches Point Clouds
  2. Also about point cloud segmentation
  3. PointNet
  4. PointNet++ from Stanford
  5. PointNet++
  6. RangeNet++


Obstacle detection:

  1. Obstacle Detection and Avoidance System for Drones
  2. 3D Lidar-based Static and Moving Obstacle Detection
  3. USER-TRAINABLE OBJECT RECOGNITION SYSTEMS
  4. Real-Time Plane Segmentation and Obstacle Detection


Useful Github links:

  1. https://github.com/PRBonn/semantic-kitti-api
  2. https://github.com/jbehley/point_labeler
  3. https://github.com/daavoo/pyntcloud
  4. https://github.com/strawlab/python-pcl
  5. https://github.com/kuixu/kitti_object_vis
  6. https://github.com/lilyhappily/SFND-P1-Lidar-Obstacle-Detection
  7. https://github.com/kcg2015/lidar_ground_plane_and_obstacles_detections
  8. https://github.com/enginBozkurt/LidarObstacleDetection