3D-LiDAR-YOLOv8-obb

3D LiDAR Object Detection using YOLOv8-obb (oriented bounding box). The pre-trained weights were trained on the A9-Intersection dataset [1]. The YOLOv8-obb [2] model is used to predict bounding boxes and class in a Bird'e-Eye-View image created from a LiDAR point cloud [3]. A separate ROS node for tracking the detections is provided, it is based on SORT [4], and uses rotated bounding boxes.

TODO

• Add example ROS bag
• Add documentation
• Add gif/image
• Create branch for Ubuntu 22/ROS2 Humble

Usage

1. Install ROS 2 Galactic (for Ubuntu 20.04)
2. Clone this repo to your ROS 2 workspace
3. Clone https://github.com/adrian-soch/ros2_numpy to your ROS 2 workspace
4. Build and run the nodes, see the example launch file for details.

Exporting model to TensorRT (Spped up inference for Nvidia)

Assuming the ultralytics package is installed, run this command with the PyTorch weights to compile an .engine file for faster inference speeds on Nvidia GPUs.

yolo export model=yolo8n-obb_range.pt format=engine imgsz=1024 half=True simplify=True

References

• [1]

  @inproceedings{zimmer2023tumtrafintersection,
          title={TUMTraf Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception},
          author={Zimmer, Walter and Cre{\ss}, Christian and Nguyen, Huu Tung and Knoll, Alois C},
          booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
          pages={1030--1037},
          year={2023},
          organization={IEEE}
      }
  

• [2]

  @software{Jocher_Ultralytics_YOLO_2023,
  author = {Jocher, Glenn and Chaurasia, Ayush and Qiu, Jing},
  license = {AGPL-3.0},
  month = jan,
  title = {{Ultralytics YOLO}},
  url = {https://github.com/ultralytics/ultralytics},
  version = {8.0.0},
  year = {2023}
  }
  

• [3]

  @misc{Super-Fast-Accurate-3D-Object-Detection-PyTorch,
  author =       {Nguyen Mau Dung},
  title =        {{Super-Fast-Accurate-3D-Object-Detection-PyTorch}},
  howpublished = {\url{https://github.com/maudzung/Super-Fast-Accurate-3D-Object-Detection}},
  year =         {2020}
  }
  

• [4]

  @inproceedings{Bewley2016_sort,
  author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
  booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
  title={Simple online and realtime tracking},
  year={2016},
  pages={3464-3468},
  keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},
  doi={10.1109/ICIP.2016.7533003}
  }