lidar_centerpoint

Purpose

lidar_centerpoint is a package for detecting dynamic 3D objects.

Inner-workings / Algorithms

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.

Inputs / Outputs

Input

Name	Type	Description
`~/input/pointcloud`	`sensor_msgs::msg::PointCloud2`	input pointcloud

Output

Name	Type	Description
`~/output/objects`	`autoware_auto_perception_msgs::msg::DetectedObjects`	detected objects

Parameters

Core Parameters

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

Assumptions / Known limits

The object.existence_probability is stored the value of classification confidence of a DNN, not probability.

(Optional) Error detection and handling

(Optional) Performance characterization

References/External links

[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

[6] https://github.com/yukkysaito/autoware_perception

[7] https://github.com/NVIDIA-AI-IOT/CUDA-PointPillars