lidar_centerpoint_tvm#
Design#
Usage#
lidar_centerpoint_tvm is a package for detecting dynamic 3D objects using TVM compiled centerpoint module for different backends. To use this package, replace lidar_centerpoint with lidar_centerpoint_tvm in perception launch files(for example, lidar_based_detection.launch.xml is lidar based detection is chosen.).
Neural network#
This package will not build without a neural network for its inference.
The network is provided by the tvm_utility package.
See its design page for more information on how to enable downloading pre-compiled networks (by setting the DOWNLOAD_ARTIFACTS cmake variable), or how to handle user-compiled networks.
Backend#
The backend used for the inference can be selected by setting the lidar_centerpoint_tvm_BACKEND cmake variable.
The current available options are llvm for a CPU backend, and vulkan or opencl for a GPU backend.
It defaults to llvm.
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 | 0.1 |
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 |
Bounding Box#
The lidar segmentation node establishes a bounding box for the detected obstacles.
The L-fit method of fitting a bounding box to a cluster is used for that.
Limitation and Known Issue#
Due to an accuracy issue of centerpoint model, vulkan cannot be used at the moment.
As for 'llvm' backend, real-time performance cannot be achieved.
Scatter Implementation#
Scatter function can be implemented using either TVMScript or C++. For C++ implementation, please refer to https://github.com/angry-crab/autoware.universe/blob/c020419fe52e359287eccb1b77e93bdc1a681e24/perception/lidar_centerpoint_tvm/lib/network/scatter.cpp#L65
Reference#
[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/Abraham423/CenterPoint
[5] https://github.com/open-mmlab/OpenPCDet
Related issues#
-
908: Run Lidar Centerpoint with TVM#