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Package Summary
| Tags | No category tags. |
| Version | 0.1.0 |
| License | Apache 2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Description | |
| Checkout URI | https://github.com/ieiauto/autodrrt.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-09-19 |
| Dev Status | UNKNOWN |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Liu Zhengfei
- Xinyu Wang
Authors
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
Wiki Tutorials
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/lidar_centerpoint_tvm.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- output/objects [default: objects]
- model_name [default: centerpoint]
- model_param_path [default: $(find-pkg-share lidar_centerpoint_tvm)/config/$(var model_name).param.yaml]
- score_threshold [default: 0.35]
- yaw_norm_threshold [default: 0.0]
- has_twist [default: false]
- data_path [default: $(env HOME)/autoware_data]
- launch/single_inference_lidar_centerpoint_tvm.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- output/objects [default: objects]
- model_name [default: centerpoint]
- model_param_path [default: $(find-pkg-share lidar_centerpoint_tvm)/config/$(var model_name).param.yaml]
- score_threshold [default: 0.35]
- yaw_norm_threshold [default: 0.0]
- has_twist [default: false]
- pcd_path [default: test.pcd]
- detections_path [default: test.ply]
- data_path [default: $(env HOME)/autoware_data]