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
`debug/cyclic_time_ms`	`tier4_debug_msgs::msg::Float64Stamped`	cyclic time (msg)
`debug/processing_time_ms`	`tier4_debug_msgs::msg::Float64Stamped`	processing time (ms)

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
`nms_iou_target_class_names`	list[string]	-	target classes for IoU-based Non Maximum Suppression
`nms_iou_search_distance_2d`	double	-	If two objects are farther than the value, NMS isn't applied.
`nms_iou_threshold`	double	-	IoU threshold for the IoU-based Non Maximum Suppression
`build_only`	bool	`false`	shutdown the node after TensorRT engine file is built

Assumptions / Known limits#

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

Trained Models#

You can download the onnx format of trained models by clicking on the links below.

Centerpoint : pts_voxel_encoder_centerpoint.onnx, pts_backbone_neck_head_centerpoint.onnx
Centerpoint tiny: pts_voxel_encoder_centerpoint_tiny.onnx, pts_backbone_neck_head_centerpoint_tiny.onnx

Centerpoint was trained in nuScenes (~28k lidar frames) [8] and TIER IV's internal database (~11k lidar frames) for 60 epochs. Centerpoint tiny was trained in Argoverse 2 (~110k lidar frames) [9] and TIER IV's internal database (~11k lidar frames) for 20 epochs.

Standalone inference and visualization#

In addition to its use as a standard ROS node, lidar_centerpoint can also be used to perform inferences in an isolated manner. To do so, execute the following launcher, where pcd_path is the path of the pointcloud to be used for inference.

ros2 launch lidar_centerpoint single_inference_lidar_centerpoint.launch.xml pcd_path:=test_pointcloud.pcd detections_path:=test_detections.ply

lidar_centerpoint generates a ply file in the provided detections_path, which contains the detections as triangle meshes. These detections can be visualized by most 3D tools, but we also integrate a visualization UI using Open3D which is launched alongside lidar_centerpoint.

Changelog#

v1 (2022/07/06)#

Name	URLs	Description
`centerpoint`	pts_voxel_encoder pts_backbone_neck_head	There is a single change due to the limitation in the implementation of this package. `num_filters=[32, 32]` of `PillarFeatureNet`
`centerpoint_tiny`	pts_voxel_encoder pts_backbone_neck_head	The same model as `default` of `v0`.

These changes are compared with this configuration.

v0 (2021/12/03)#

Name	URLs	Description
`default`	pts_voxel_encoder pts_backbone_neck_head	There are two changes from the original CenterPoint architecture. `num_filters=[32]` of `PillarFeatureNet` and `ds_layer_strides=[2, 2, 2]` of `RPN`

(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

[8] https://www.nuscenes.org/nuscenes

[9] https://www.argoverse.org/av2.html