Modified by cdefg

THIS repository is not NVIDIA's original repository.

Setup and Run

Prerequisites: ROS2(tested on galactic, Ubuntu 22.04LTS)
rclcpp
sensor_msgs
CUDA >= 11.3
TensorRT >= 8.4

To compile:

colcon build

Essential Info to adapt to ROS2 system

model IO

Original repo uses data from .bin binary files. But to build a stream processing package, this package adapt loadData() function from main.cpp. The PointPillars model input takes serialized lidar data. the data is serialized point by point. The points are featured and the domains must be 'x', 'y', 'z', 'intensity'. As the PointPillars algorithm input only takes original $point$ infomation, the only thing we should do is to feed the PointCloud2 data (from ROS2) and size (row_step*height, stated from PointCloud2 msg in ros.org) into data C++ pointer.

ament building system for CUDA

Another tough thing to adapt to ROS2 is to build with CUDA, especially ROS2's ament system. Yet finally I succeeded in editing CMakeLists.txt and build with colcon build command.

for cuda

find_package(CUDA REQUIRED)

Get right path for tensorrt

set(TENSORRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu/)
set(TENSORRT_LIBRARY_DIRS /usr/lib/x86_64-linux-gnu/)

Link use cuda_add_executable or cuda_add_library

cuda_add_executable(pc_process src/pc_process.cpp src/cuda_pp_ros.cpp
src/cuda_pp_ros.cpp
src/pillarScatter.cpp
src/pointpillar.cpp
src/postprocess.cpp
src/preprocess.cpp

src/postprocess_kernels.cu
src/pillarScatterKernels.cu
src/preprocess_kernels.cu
)

ORIGINAL Essentials Info from Nv's repo

PointPillars Inference with TensorRT

Original Readme.md file can be found at https://github.com/NVIDIA-AI-IOT/CUDA-PointPillars
$Below$ $is$ $lite$ $version.$

This repository contains sources and model for pointpillars inference using TensorRT. The model is created with OpenPCDet and modified with onnx_graphsurgeon.

Overall inference has four phases:

• Convert points cloud into 4-channle voxels
• Extend 4-channel voxels to 10-channel voxel features
• Run TensorRT engine to get 3D-detection raw data
• Parse bounding box, class type and direction

Model && Data

The demo use the velodyne data from KITTI Dataset. The onnx file can be converted from pre-trained model with given script under "./tool".

Prerequisites

To build the pointpillars inference, TensorRT with PillarScatter layer and CUDA are needed. PillarScatter layer plugin is already implemented as a plugin for TRT in the demo.

Note

• GenerateVoxels has random output since GPU processes all points simultaneously while points selection for a voxel is random.
• The demo will cache the onnx file to improve performance. If a new onnx will be used, please remove the cache file in "./model".
• MAX_VOXELS in params.h is used to allocate cache during inference. Decrease the value to save memory.

References

• PointPillars: Fast Encoders for Object Detection from Point Clouds