lidar_centerpoint is a package for detecting dynamic 3D objects.
In this implementation, CenterPoint [1] uses a PointPillars-based [2] network to inference with TensorRT.
We trained the models using https://github.com/open-mmlab/mmdetection3d.
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
input pointcloud |
Name | Type | Description |
---|---|---|
~/output/objects |
autoware_auto_perception_msgs::msg::DetectedObjects |
detected objects |
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
float | 0.4 |
detected objects with score less than threshold are ignored |
densification_world_frame_id |
string | map |
the world frame id to fuse multi-frame pointcloud |
densification_num_past_frames |
int | 1 |
the number of past frames to fuse with the current frame |
trt_precision |
string | fp16 |
TensorRT inference precision: fp32 or fp16 |
encoder_onnx_path |
string | "" |
path to VoxelFeatureEncoder ONNX file |
encoder_engine_path |
string | "" |
path to VoxelFeatureEncoder TensorRT Engine file |
head_onnx_path |
string | "" |
path to DetectionHead ONNX file |
head_engine_path |
string | "" |
path to DetectionHead TensorRT Engine file |
- The
object.existence_probability
is stored the value of classification confidence of a DNN, not probability.
[1] Yin, Tianwei, Xingyi Zhou, and Philipp Krähenbühl. "Center-based 3d object detection and tracking." arXiv preprint arXiv:2006.11275 (2020).
[2] Lang, Alex H., et al. "Pointpillars: Fast encoders for object detection from point clouds." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
[3] https://github.com/tianweiy/CenterPoint
[4] https://github.com/open-mmlab/mmdetection3d
[5] https://github.com/open-mmlab/OpenPCDet