PointCloudSegmentation

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<img src="https://github.com/VirtualRoyalty/PointCloudSegmentation/blob/master/obstacle-detection/examples/img/MainGifwithLabels.gif" width="1000">

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Project structure:

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

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How to dockerize this:

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• 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:<br><br> $ docker run -it --rm -p 8888:8888 -v /path/to/obstacle-detection:/home/jovyan/work jupyter <br><br> NOTE: on Windows you need to convert your path into a quasi-Linux format (e.g. //c/path/to/obstacle-detection). More details here <br> 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 <br><br>
• After correct running you will see URL to access jupyter, e.g.: <br><br> httр://127.0.0.1:8888?token=0cccd15e74216ed2dbe681738ed0f9c78bf65515e94f27a8<br><br>
• To access jupyter you need to go for Docker IP:8888?token=xxxx... <br>( e.g. httр://192.168.99.100:8888/?token=0cccd15e74216ed2dbe681738ed0f9c78bf65515e94f27a8)<br><br>
• To enter a docker container run $ docker exec -it *CONTAINER ID* bash (find out ID by running $ docker ps)

Pre-trained Models

SemanticKITTI

• squeezeseg
• squeezeseg + crf
• squeezesegV2
• squeezesegV2 + crf
• darknet21
• darknet53
• darknet53-1024
• darknet53-512

References and useful links:

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<br>Dataset:

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

<br>Segmentation:

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

<br> Obstacle detection:

9. Obstacle Detection and Avoidance System for Drones
10. 3D Lidar-based Static and Moving Obstacle Detection
11. USER-TRAINABLE OBJECT RECOGNITION SYSTEMS
12. Real-Time Plane Segmentation and Obstacle Detection

<br> Useful Github links:

13. https://github.com/PRBonn/semantic-kitti-api
14. https://github.com/jbehley/point_labeler
15. https://github.com/daavoo/pyntcloud
16. https://github.com/strawlab/python-pcl
17. https://github.com/kuixu/kitti_object_vis
18. https://github.com/lilyhappily/SFND-P1-Lidar-Obstacle-Detection
19. https://github.com/kcg2015/lidar_ground_plane_and_obstacles_detections
20. https://github.com/enginBozkurt/LidarObstacleDetection