Project structure:
├───docker-env/
├───obstacle-detection/
│ ├───dataset/
│ │ └───sequences/
│ │ └───00/
│ │ ├───clusters/
│ │ ├───labels/
│ │ └───velodyne/
| ├───model/
| |
│ ├───examples/
│ │
│ ├───pipeline/
│ │
│ └───scripts/
│
└───visualization/
- In docker-env/ 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
)
- squeezeseg
- squeezeseg + crf
- squeezesegV2
- squeezesegV2 + crf
- darknet21
- darknet53
- darknet53-1024
- darknet53-512
Dataset:
Segmentation:
- Segmentation approaches Point Clouds
- Also about point cloud segmentation
- PointNet
- PointNet++ from Stanford
- PointNet++
- RangeNet++
Obstacle detection:
- Obstacle Detection and Avoidance System for Drones
- 3D Lidar-based Static and Moving Obstacle Detection
- USER-TRAINABLE OBJECT RECOGNITION SYSTEMS
- Real-Time Plane Segmentation and Obstacle Detection
Useful Github links:
- https://github.com/PRBonn/semantic-kitti-api
- https://github.com/jbehley/point_labeler
- https://github.com/daavoo/pyntcloud
- https://github.com/strawlab/python-pcl
- https://github.com/kuixu/kitti_object_vis
- https://github.com/lilyhappily/SFND-P1-Lidar-Obstacle-Detection
- https://github.com/kcg2015/lidar_ground_plane_and_obstacles_detections
- https://github.com/enginBozkurt/LidarObstacleDetection