Upgrade code to Humble

.gitignore

@@ -0,0 +1,3 @@
+build/*
+log/*
+install/*

CMakeLists.txt

@@ -24,9 +24,9 @@ if(NOT CMAKE_C_STANDARD)
   set(CMAKE_C_STANDARD 99)
 endif()
 
-# Default to C++14
+# Default to C++17
 if(NOT CMAKE_CXX_STANDARD)
-  set(CMAKE_CXX_STANDARD 14)
+  set(CMAKE_CXX_STANDARD 17)
 endif()
 
 if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
@@ -42,15 +42,22 @@ find_package(rclcpp REQUIRED)
 find_package(std_msgs REQUIRED)
 find_package(vision_msgs REQUIRED)
 find_package(geometry_msgs REQUIRED)
+find_package(sensor_msgs REQUIRED)
+find_package(tf2_ros REQUIRED)
+find_package(tf2_geometry_msgs REQUIRED)
+find_package(message_filters REQUIRED)
 find_package(PCL REQUIRED)
+find_package(pcl_conversions REQUIRED)
 find_package(CUDA REQUIRED)
 
-set(CUDA_VERSION 11.3)
+
+# change here according to your version
+set(CUDA_VERSION 11.6)
 set(CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda-${CUDA_VERSION})
 
 SET(CMAKE_BUILD_TYPE "Release")
 add_compile_options(-W)
-add_compile_options(-std=c++11)
+# add_compile_options(-std=c++17)
 
 set(SMS 50 52 53 60 61 62 70 72 75 80 86)
 foreach(sm ${SMS})
@@ -70,12 +77,14 @@ set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
     -Xlinker -L/usr/${ARCH}-linux-gnu/lib/
 )
 
-set(TENSORRT_INCLUDE_DIRS /usr/include/${ARCH}-linux-gnu/)
-set(TENSORRT_LIBRARY_DIRS /usr/lib/${ARCH}-linux-gnu/)
+set(TENSORRT_INCLUDE_DIRS ~/TensorRT/include/)
+set(TENSORRT_ONNX_INCLUDE_DIRS ~/TensorRT/parsers/onnx/)
+set(TENSORRT_LIBRARY_DIRS  ~/TensorRT-8.4.1.5/lib/)
 
 include_directories(
     ${CUDA_INCLUDE_DIRS}
     ${TENSORRT_INCLUDE_DIRS}
+    ${TENSORRT_ONNX_INCLUDE_DIRS}
     ../include/
 )
 
@@ -92,7 +101,7 @@ file(GLOB_RECURSE SOURCE_FILES
 
 
 cuda_add_executable(${PROJECT_NAME} src/pp_inference_node.cpp ${SOURCE_FILES})
-ament_target_dependencies(${PROJECT_NAME} rclcpp std_msgs vision_msgs geometry_msgs PCL CUDA)
+ament_target_dependencies(${PROJECT_NAME} rclcpp std_msgs vision_msgs geometry_msgs sensor_msgs message_filters tf2_geometry_msgs tf2_ros pcl_conversions PCL CUDA)
 
 target_link_libraries(${PROJECT_NAME}
     libnvinfer.so

README.md

@@ -16,9 +16,14 @@ Node details:
 </p>
 
 ## Requirements
-Tested on Ubuntu 20.04 and ROS2 Foxy.
+Tested on Ubuntu 22.04 and ROS2 Humble.
+- pcl library 
 - TensorRT 8.2(or above)
-- TensorRT OSS 22.02 (see how to install below)
+- TensorRT OSS 22.02 (or above)
+```
+sudo apt install libpcl-dev
+```
+
 ```
 git clone -b 22.02 https://github.com/NVIDIA/TensorRT.git TensorRT
 cd TensorRT
@@ -31,9 +36,11 @@ cp libnvinfer_plugin.so.8.2.* /usr/lib/$ARCH-linux-gnu/libnvinfer_plugin.so.8.2.
 cp libnvinfer_plugin_static.a /usr/lib/$ARCH-linux-gnu/libnvinfer_plugin_static.a
 ```
 - [TAO Converter](https://docs.nvidia.com/tao/tao-toolkit/text/tensorrt.html#installing-the-tao-converter)
-- [ROS2 Foxy](https://docs.ros.org/en/foxy/Installation.html)
+- [ROS2 Humble](https://docs.ros.org/en/humble/Installation.html)
 
 ## Usage
+### Cuda version
+Change the CMakeLists.txt 
 
 1. This project assumes that you have already trained your model using NVIDIA TAO Toolkit and have an **.etlt** file. If not, please refer [here](https://docs.nvidia.com/tao/tao-toolkit/text/point_cloud/index.html) for information on how to do this. The pre-trained PointPillars model used by this project can be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet/files).
 2. Use **tao-converter** to generate a TensorRT engine from your model. For instance:
@@ -54,8 +61,8 @@ Argument definitions:
 - -t: Desired engine data type. The options are fp32 or fp16 (default value is fp32). 
 
 3. Source your ROS2 environment:
-`source /opt/ros/foxy/setup.bash`
-4. Create a ROS2 workspace (more information can be found [here](https://docs.ros.org/en/foxy/Tutorials/Beginner-Client-Libraries/Creating-A-Workspace/Creating-A-Workspace.html)):
+`source /opt/ros/humble/setup.bash`
+4. Create a ROS2 workspace (more information can be found [here](https://docs.ros.org/en/humble/Tutorials/Beginner-Client-Libraries/Creating-A-Workspace/Creating-A-Workspace.html)):
 ```
 mkdir -p pointpillars_ws/src
 cd pointpillars_ws/src
@@ -73,7 +80,7 @@ Clone this repository in `pointpillars_ws/src`. The directory structure should l
 ```
 5. Resolve missing dependencies by running the following command from `pointpillars_ws`:
 
-`rosdep install -i --from-path src --rosdistro foxy -y`
+`rosdep install -i --from-path src --rosdistro humble -y`
 
 6. Specify parameters including the path to your TensorRT engine in the launch file. Please see [Modifying parameters in the launch file](https://github.com/NVIDIA-AI-IOT/ros2_tao_pointpillars#modifying-parameters-in-the-launch-file) below for how to do this.
 

include/pp_infer/pointpillar.h

@@ -92,7 +92,7 @@ class PointPillar {
     ~PointPillar(void);
     int getPointSize();
     std::vector<Bndbox> doinfer(
-      void*points_data,
+      void* points_data,
       unsigned int* points_size,
       std::vector<Bndbox> &nms_pred,
       float nms_iou_thresh,

src/pp_inference_node.cpp

@@ -43,7 +43,7 @@
 #include "sensor_msgs/msg/point_cloud2.hpp"
 #include "vision_msgs/msg/detection3_d_array.hpp"
 #include "pcl_conversions/pcl_conversions.h"
-#include "../include/pp_infer/point_cloud2_iterator.hpp"
+#include "sensor_msgs/point_cloud2_iterator.hpp"
 #include <tf2/LinearMath/Quaternion.h>
 #include <tf2_geometry_msgs/tf2_geometry_msgs.h>
 
@@ -73,7 +73,7 @@ class MinimalPublisher : public rclcpp::Node
   MinimalPublisher()
   : Node("minimal_publisher")
   {
-    this->declare_parameter("class_names");
+    this->declare_parameter<std::vector<std::string>>("class_names");
     this->declare_parameter<float>("nms_iou_thresh", 0.01);
     this->declare_parameter<int>("pre_nms_top_n", 4096);
     this->declare_parameter<std::string>("model_path", "");
@@ -94,10 +94,13 @@ class MinimalPublisher : public rclcpp::Node
     cudaStream_t stream = NULL;
     pointpillar = new PointPillar(model_path, engine_path, stream, data_type);
 
-    publisher_ = this->create_publisher<vision_msgs::msg::Detection3DArray>("bbox", 700);
+    publisher_ = this->create_publisher<vision_msgs::msg::Detection3DArray>("bbox", 1);
+
+    //QoS: sensor profile
+    static const rclcpp::QoS sensor_qos_profile = rclcpp::QoS(5).best_effort().durability_volatile();
 
     subscription_ = this->create_subscription<sensor_msgs::msg::PointCloud2>(
-      "/point_cloud", 700, std::bind(&MinimalPublisher::topic_callback, this, _1));
+      "/point_cloud", sensor_qos_profile, std::bind(&MinimalPublisher::topic_callback, this, _1));
 
   }
 
@@ -196,8 +199,8 @@ class MinimalPublisher : public rclcpp::Node
 
         detection.bbox.center.orientation = orientation;
 
-        hyp.id = std::to_string(nms_pred[i].id);
-        hyp.score = nms_pred[i].score;
+        hyp.hypothesis.class_id = std::to_string(nms_pred[i].id);
+        hyp.hypothesis.score = nms_pred[i].score;
 
         detection.header = msg->header;