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amadeuszszpre-commit-ci[bot]knzo25
authoredJun 7, 2024
feat(lidar_transfusion): add lidar_transfusion 3D detection package (autowarefoundation#6890)
* feat(lidar_transfusion): add lidar_transfusion 3D detection package Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * style(pre-commit): autofix * style(lidar_transfusion): cpplint Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * style(lidar_transfusion): cspell Signed-off-by: Amadeusz Szymko <amadeusz.szymko@tier4.jp> * fix(lidar_transfusion): CUDA mem allocation & inference input Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * style(pre-commit): autofix * fix(lidar_transfusion): arrays size Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * style(pre-commit): autofix * chore(lidar_transfusion): update maintainers Co-authored-by: Satoshi Tanaka <16330533+scepter914@users.noreply.github.com> Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * fix(lidar_transfusion): array size & grid idx Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * chore(lidar_transfusion): update maintainer email Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * chore: added transfusion to the respective launchers Signed-off-by: Kenzo Lobos-Tsunekawa <kenzo.lobos@tier4.jp> * refactor(lidar_transfusion): rename config Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * refactor(lidar_transfusion): callback access specifier Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * refactor(lidar_transfusion): pointers initialziation Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * refactor(lidar_transfusion): change macros for constexpr Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * refactor(lidar_transfusion): consts & uniform initialization Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * refactor(lidar_transfusion): change to unique ptr & uniform initialization Signed-off-by: amadeuszsz <amadeusz.szymko@tier4.jp> * style(pre-commit): autofix * refactor(lidar_transfusion): use of config params Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * refactor(lidar_transfusion): remove unnecessary condition Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * style(lidar_transfusion): switch naming (CPU to HOST) Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * refactor(lidar_transfusion): remove redundant device sync Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * style(lidar_transfusion): intensity naming Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * feat(lidar_transfusion): full network shape validation Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * feat(lidar_transfusion): validate objects' orientation in host processing Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * feat(lidar_transfusion): add json schema Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * style(pre-commit): autofix * style(lidar_transfusion): affine matrix naming Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * style(lidar_transfusion): transformed point naming Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * refactor(lidar_transfusion): add param descriptor & arrays size check Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * style(lidar_transfusion): affine matrix naming Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * feat(lidar_transfusion): caching cloud input as device ptr Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * fix(lidar_transfusion): logging Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * chore(tier4_perception_launch): revert to centerpoint Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * fix(lidar_transfusion): typo Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * docs(lidar_transfusion): use hook for param description Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * fix(lidar_transfusion): interpret eigen matrix as col major Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> * feat(lidar_transfusion): update to autware_msgs Signed-off-by: amadeuszsz <amadeusz.szymko.2@tier4.jp> --------- Signed-off-by: Amadeusz Szymko <amadeusz.szymko.2@tier4.jp> Signed-off-by: Kenzo Lobos-Tsunekawa <kenzo.lobos@tier4.jp> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kenzo Lobos Tsunekawa <kenzo.lobos@tier4.jp>
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‎launch/tier4_perception_launch/launch/object_recognition/detection/detection.launch.xml

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<!-- Lidar parameters -->
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<arg name="input/pointcloud"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `centerpoint`, `apollo`, `pointpainting`, `clustering`"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `transfusion`, `centerpoint`, `apollo`, `pointpainting`, `clustering`"/>
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<arg name="use_object_filter" default="true" description="use object filter"/>
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<arg name="pointcloud_container_name" default="pointcloud_container"/>
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‎launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_dnn_detector.launch.xml

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<launch>
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<!-- Lidar parameters -->
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<arg name="input/pointcloud"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `centerpoint`, `apollo`, `clustering`"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `transfusion`, `centerpoint`, `apollo`, `clustering`"/>
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<!-- Lidar detector centerpoint parameters -->
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<arg name="centerpoint_model_name" default="centerpoint_tiny" description="options: `centerpoint`, `centerpoint_tiny` or `centerpoint_sigma`"/>
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<arg name="centerpoint_model_path" default="$(var data_path)/lidar_centerpoint"/>
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<arg name="lidar_model_param_path" default="$(find-pkg-share lidar_centerpoint)/config"/>
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<!-- Lidar detector transfusion parameters -->
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<arg name="transfusion_model_name" default="transfusion" description="options: `transfusion`"/>
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<arg name="transfusion_model_path" default="$(var data_path)/lidar_transfusion"/>
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<!-- TransFusion -->
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<group if="$(eval &quot;'$(var lidar_detection_model)'=='transfusion'&quot;)">
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<push-ros-namespace namespace="transfusion"/>
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<arg name="lidar_model_param_path" default="$(find-pkg-share lidar_transfusion)/config"/>
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<group>
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<include file="$(find-pkg-share lidar_transfusion)/launch/lidar_transfusion.launch.xml">
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<arg name="input/pointcloud" value="$(var input/pointcloud)"/>
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<arg name="output/objects" value="objects"/>
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<arg name="model_name" value="$(var transfusion_model_name)"/>
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<arg name="model_path" value="$(var transfusion_model_path)"/>
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<arg name="model_param_path" value="$(var lidar_model_param_path)/$(var transfusion_model_name).param.yaml"/>
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<arg name="class_remapper_param_path" value="$(var lidar_model_param_path)/detection_class_remapper.param.yaml"/>
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<arg name="use_pointcloud_container" value="true"/>
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<arg name="pointcloud_container_name" value="$(var pointcloud_container_name)"/>
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</include>
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</group>
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</group>
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<!-- CenterPoint -->
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<group if="$(eval &quot;'$(var lidar_detection_model)'=='centerpoint'&quot;)">
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<push-ros-namespace namespace="centerpoint"/>
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<arg name="lidar_model_param_path" default="$(find-pkg-share lidar_centerpoint)/config"/>
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<group>
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<include file="$(find-pkg-share lidar_centerpoint)/launch/lidar_centerpoint.launch.xml">
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<arg name="input/pointcloud" value="$(var input/pointcloud)"/>

‎launch/tier4_perception_launch/launch/perception.launch.xml

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<arg name="input/pointcloud" default="/sensing/lidar/concatenated/pointcloud" description="The topic will be used in the detection module"/>
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<arg name="mode" default="camera_lidar_fusion" description="options: `camera_lidar_radar_fusion`, `camera_lidar_fusion`, `lidar_radar_fusion`, `lidar` or `radar`"/>
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<arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `centerpoint`, `apollo`, `pointpainting`, `clustering`"/>
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<arg name="lidar_detection_model" default="centerpoint" description="options: `transfusion`, `centerpoint`, `apollo`, `pointpainting`, `clustering`"/>
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<arg name="image_raw0" default="/sensing/camera/camera0/image_rect_color" description="image raw topic name"/>
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<arg name="camera_info0" default="/sensing/camera/camera0/camera_info" description="camera info topic name"/>
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<arg name="detection_rois0" default="/perception/object_recognition/detection/rois0" description="detection rois output topic name"/>

‎perception/lidar_transfusion/CMakeLists.txt

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cmake_minimum_required(VERSION 3.14)
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project(lidar_transfusion)
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find_package(autoware_cmake REQUIRED)
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autoware_package()
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add_compile_options(-Wno-deprecated-declarations)
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option(CUDA_VERBOSE "Verbose output of CUDA modules" OFF)
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# set flags for CUDA availability
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option(CUDA_AVAIL "CUDA available" OFF)
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find_package(CUDA)
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if(CUDA_FOUND)
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find_library(CUBLAS_LIBRARIES cublas HINTS
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${CUDA_TOOLKIT_ROOT_DIR}/lib64
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${CUDA_TOOLKIT_ROOT_DIR}/lib
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)
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if(CUDA_VERBOSE)
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message("CUDA is available!")
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message("CUDA Libs: ${CUDA_LIBRARIES}")
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message("CUDA Headers: ${CUDA_INCLUDE_DIRS}")
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endif()
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# Note: cublas_device was depreciated in CUDA version 9.2
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# https://forums.developer.nvidia.com/t/where-can-i-find-libcublas-device-so-or-libcublas-device-a/67251/4
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# In LibTorch, CUDA_cublas_device_LIBRARY is used.
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unset(CUDA_cublas_device_LIBRARY CACHE)
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set(CUDA_AVAIL ON)
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else()
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message("CUDA NOT FOUND")
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set(CUDA_AVAIL OFF)
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endif()
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# set flags for TensorRT availability
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option(TRT_AVAIL "TensorRT available" OFF)
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# try to find the tensorRT modules
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find_library(NVINFER nvinfer)
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find_library(NVONNXPARSER nvonnxparser)
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if(NVINFER AND NVONNXPARSER)
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if(CUDA_VERBOSE)
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message("TensorRT is available!")
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message("NVINFER: ${NVINFER}")
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message("NVONNXPARSER: ${NVONNXPARSER}")
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endif()
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set(TRT_AVAIL ON)
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else()
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message("TensorRT is NOT Available")
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set(TRT_AVAIL OFF)
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endif()
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# set flags for CUDNN availability
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option(CUDNN_AVAIL "CUDNN available" OFF)
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# try to find the CUDNN module
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find_library(CUDNN_LIBRARY
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NAMES libcudnn.so${__cudnn_ver_suffix} libcudnn${__cudnn_ver_suffix}.dylib ${__cudnn_lib_win_name}
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PATHS $ENV{LD_LIBRARY_PATH} ${__libpath_cudart} ${CUDNN_ROOT_DIR} ${PC_CUDNN_LIBRARY_DIRS} ${CMAKE_INSTALL_PREFIX}
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PATH_SUFFIXES lib lib64 bin
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DOC "CUDNN library."
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)
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if(CUDNN_LIBRARY)
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if(CUDA_VERBOSE)
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message(STATUS "CUDNN is available!")
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message(STATUS "CUDNN_LIBRARY: ${CUDNN_LIBRARY}")
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endif()
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set(CUDNN_AVAIL ON)
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else()
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message("CUDNN is NOT Available")
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set(CUDNN_AVAIL OFF)
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endif()
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if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL)
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find_package(ament_cmake_auto REQUIRED)
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ament_auto_find_build_dependencies()
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include_directories(
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include
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${CUDA_INCLUDE_DIRS}
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)
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if(CMAKE_BUILD_TYPE STREQUAL "Debug")
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set(CMAKE_CUDA_FLAGS ${CMAKE_CUDA_FLAGS} "-g -G")
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set(CUDA_NVCC_FLAGS "-g -G")
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endif()
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ament_auto_add_library(transfusion_lib SHARED
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lib/detection_class_remapper.cpp
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lib/network/network_trt.cpp
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lib/postprocess/non_maximum_suppression.cpp
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lib/preprocess/voxel_generator.cpp
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lib/preprocess/pointcloud_densification.cpp
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lib/ros_utils.cpp
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lib/transfusion_trt.cpp
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)
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cuda_add_library(transfusion_cuda_lib SHARED
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lib/postprocess/circle_nms_kernel.cu
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lib/postprocess/postprocess_kernel.cu
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lib/preprocess/preprocess_kernel.cu
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)
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target_link_libraries(transfusion_lib
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${NVINFER}
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${NVONNXPARSER}
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${CUDA_LIBRARIES}
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${CUBLAS_LIBRARIES}
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${CUDA_curand_LIBRARY}
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${CUDNN_LIBRARY}
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transfusion_cuda_lib
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)
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target_include_directories(transfusion_lib
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PUBLIC
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$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
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$<INSTALL_INTERFACE:include>
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)
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# To suppress unknown-pragmas error. The root-cause is CUB library in CUDA 11.6.
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# This issue was fixed by https://github.com/NVIDIA/cub/commit/7d608bf1dc14553e2fb219eabeed80b76621b6fe
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target_include_directories(transfusion_lib
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SYSTEM PUBLIC
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${CUDA_INCLUDE_DIRS}
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)
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ament_auto_add_library(lidar_transfusion_component SHARED
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src/lidar_transfusion_node.cpp
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)
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target_link_libraries(lidar_transfusion_component
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transfusion_lib
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)
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rclcpp_components_register_node(lidar_transfusion_component
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PLUGIN "lidar_transfusion::LidarTransfusionNode"
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EXECUTABLE lidar_transfusion_node
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)
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if(BUILD_TESTING)
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find_package(ament_lint_auto REQUIRED)
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ament_lint_auto_find_test_dependencies()
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endif()
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ament_auto_package(
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INSTALL_TO_SHARE
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launch
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config
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)
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else()
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find_package(ament_cmake_auto REQUIRED)
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ament_auto_find_build_dependencies()
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ament_auto_package(
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INSTALL_TO_SHARE
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launch
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)
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endif()

‎perception/lidar_transfusion/README.md

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# lidar_transfusion
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## Purpose
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The `lidar_transfusion` package is used for 3D object detection based on lidar data (x, y, z, intensity).
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## Inner-workings / Algorithms
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The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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We trained the models using <https://github.com/open-mmlab/mmdetection3d>.
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## Inputs / Outputs
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### Input
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| Name | Type | Description |
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| -------------------- | ------------------------------- | ----------------- |
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| `~/input/pointcloud` | `sensor_msgs::msg::PointCloud2` | Input pointcloud. |
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### Output
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| Name | Type | Description |
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| -------------------------------------- | ------------------------------------------------ | --------------------------- |
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| `~/output/objects` | `autoware_perception_msgs::msg::DetectedObjects` | Detected objects. |
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| `debug/cyclic_time_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Cyclic time (ms). |
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| `debug/pipeline_latency_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Pipeline latency time (ms). |
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| `debug/processing_time/preprocess_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Preprocess (ms). |
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| `debug/processing_time/inference_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Inference time (ms). |
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| `debug/processing_time/postprocess_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Postprocess time (ms). |
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| `debug/processing_time/total_ms` | `tier4_debug_msgs::msg::Float64Stamped` | Total processing time (ms). |
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## Parameters
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### TransFusion
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{{ json_to_markdown("perception/lidar_transfusion/schema/transfusion.schema.json") }}
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### Detection class remapper
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{{ json_to_markdown("perception/lidar_transfusion/schema/detection_class_remapper.schema.json") }}
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### The `build_only` option
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The `lidar_transfusion` node has `build_only` option to build the TensorRT engine file from the ONNX file.
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Although it is preferred to move all the ROS parameters in `.param.yaml` file in Autoware Universe, the `build_only` option is not moved to the `.param.yaml` file for now, because it may be used as a flag to execute the build as a pre-task. You can execute with the following command:
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```bash
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ros2 launch lidar_transfusion lidar_transfusion.launch.xml build_only:=true
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```
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### The `log_level` option
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The default logging severity level for `lidar_transfusion` is `info`. For debugging purposes, the developer may decrease severity level using `log_level` parameter:
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```bash
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ros2 launch lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
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```
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## Assumptions / Known limits
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## Trained Models
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You can download the onnx format of trained models by clicking on the links below.
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- TransFusion: [transfusion.onnx](https://awf.ml.dev.web.auto/perception/models/transfusion/v1/transfusion.onnx)
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The model was trained in TIER IV's internal database (~11k lidar frames) for 20 epochs.
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### Changelog
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## (Optional) Error detection and handling
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<!-- Write how to detect errors and how to recover from them.
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Example:
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This package can handle up to 20 obstacles. If more obstacles found, this node will give up and raise diagnostic errors.
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-->
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## (Optional) Performance characterization
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<!-- Write performance information like complexity. If it wouldn't be the bottleneck, not necessary.
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Example:
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### Complexity
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This algorithm is O(N).
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### Processing time
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...
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-->
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## References/External links
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[1] Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai. "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers." arXiv preprint arXiv:2203.11496 (2022). <!-- cspell:disable-line -->
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[2] <https://github.com/wep21/CUDA-TransFusion>
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[3] <https://github.com/open-mmlab/mmdetection3d>
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[4] <https://github.com/open-mmlab/OpenPCDet>
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[5] <https://www.nuscenes.org/nuscenes>
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## (Optional) Future extensions / Unimplemented parts
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<!-- Write future extensions of this package.
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Example:
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Currently, this package can't handle the chattering obstacles well. We plan to add some probabilistic filters in the perception layer to improve it.
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Also, there are some parameters that should be global(e.g. vehicle size, max steering, etc.). These will be refactored and defined as global parameters so that we can share the same parameters between different nodes.
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-->

‎perception/lidar_transfusion/config/detection_class_remapper.param.yaml

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/**:
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ros__parameters:
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allow_remapping_by_area_matrix:
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# NOTE(kl): We turn all vehicles into trailers if they go over 3x12 [m^2].
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# NOTE(kl): We turn cars into trucks if they have an area between 2.2 x 5.5 and 3.0 * 12.0 [m^2]
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# row: original class. column: class to remap to
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#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE,PEDESTRIAN
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[0, 0, 0, 0, 0, 0, 0, 0, #UNKNOWN
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0, 0, 1, 0, 1, 0, 0, 0, #CAR
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0, 0, 0, 0, 1, 0, 0, 0, #TRUCK
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0, 0, 0, 0, 1, 0, 0, 0, #BUS
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0, 0, 0, 0, 0, 0, 0, 0, #TRAILER
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0, 0, 0, 0, 0, 0, 0, 0, #MOTORBIKE
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0, 0, 0, 0, 0, 0, 0, 0, #BICYCLE
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0, 0, 0, 0, 0, 0, 0, 0] #PEDESTRIAN
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min_area_matrix:
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#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
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[ 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #UNKNOWN
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0.000, 0.000, 12.100, 0.000, 36.000, 0.000, 0.000, 0.000, #CAR
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0.000, 0.000, 0.000, 0.000, 36.000, 0.000, 0.000, 0.000, #TRUCK
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0.000, 0.000, 0.000, 0.000, 36.000, 0.000, 0.000, 0.000, #BUS
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #TRAILER
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #MOTORBIKE
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #BICYCLE
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] #PEDESTRIAN
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max_area_matrix:
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#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
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[ 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #UNKNOWN
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0.000, 0.000, 36.000, 0.000, 999.999, 0.000, 0.000, 0.000, #CAR
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0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #TRUCK
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0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #BUS
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #TRAILER
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #MOTORBIKE
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #BICYCLE
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] #PEDESTRIAN

‎perception/lidar_transfusion/config/transfusion.param.yaml

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/**:
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ros__parameters:
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# network
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class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
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trt_precision: fp16
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voxels_num: [5000, 30000, 60000] # [min, opt, max]
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point_cloud_range: [-76.8, -76.8, -3.0, 76.8, 76.8, 5.0] # [x_min, y_min, z_min, x_max, y_max, z_max]
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voxel_size: [0.3, 0.3, 8.0] # [x, y, z]
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onnx_path: "$(var model_path)/transfusion.onnx"
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engine_path: "$(var model_path)/transfusion.engine"
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# pre-process params
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densification_num_past_frames: 1
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densification_world_frame_id: map
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# post-process params
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circle_nms_dist_threshold: 0.5
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iou_nms_target_class_names: ["CAR"]
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iou_nms_search_distance_2d: 10.0
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iou_nms_threshold: 0.1
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yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0] # refers to the class_names
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score_threshold: 0.2

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