Lidar 3D Object Detector Node

Maintained by Gueren Sanford and Ragib Arnab


The LiDAR Detection Node performs 3D object detection on LiDAR pointclouds. The detection must be done using raw pointcloud data to optimize the detection rate. The node has two different methods for detection:

Complex YOLOv4

The complex_yolov4_model turns pointcloud data into a birdseye view map of the points. The model uses these 2D images to form 3D detections. The implementation was adpated from this GitHub repo. Look at complex_yolov4_model for an example of how to integrate + organize a custom neural network into navigator. More information:

  • Average Hz:
    • 20.5
  • Optimal confidence threshold:
    • 0.9
  • Pros:
    • Higher accuracy
    • Many detections
  • Cons:
    • More hallucinations


The mmdetection3d_model uses pointcloud data to form 3D detections. This implementation uses the MMDetection3D API for its model intialization and inferencing. The API supports a variety of LiDAR vehicle detection models, which can be switched by changing the config_path and checkpoint_path. More information:

  • Average Hz:
    • 23.5
  • Optimal confidence threshold:
    • 0.4
  • Pros:
    • More reliable
    • Interchangable model files
  • Cons:
    • Low confidence scores


  • device str
    • The device the model will run on. Choices:
      • “cuda:0” (DEFAULT)
      • “cuda:{NUMBER}”
  • model str
    • The type of model making the detections. Choices:
      • “mmdetection3d” (DEFAULT)
      • “complex_yolo”
  • conf_thresh float
    • The mininum confidence value accepted for bounding boxes. Choices:
      • 0.7 (DEFAULT)
      • 0.0 to 1.0
  • nms_thresh float
    • The maximum accepted intersection accepted for bounding boxes. Choices:
      • 0.2 (DEFAULT)
      • 0.0 to 1.0


  • /lidar PointCloud2
    • Receives raw LiDAR point cloud data.


  • /detected/objects3d Object3DArray
    • Publishes an array of 3D objects.

lidar_callback(lidar_msg: PointCloud2)

Uses the lidar msg and a 3D object deteciton model to form 3D bouding boxes around objects. Then, publishes the boxes to a ‘detected’ topic.