Deep Grasp部署调试
X86结构移植
系统环境
OS：Ubuntu20.04
ROS：noetic apt安装
PCL部分
因为gpd用到了pcl_gpu，因此需要针对pcl进行源码定制编译，这里记录遇到的问题和解决方案。使用cmake-gui配置pcl的cmakelists，如图，打开WITH部分，选中WITH GPU和WITH CUDA。
编译是遇到了问题，说compute_30不支持了，这个时候如下图，在CUDA里面把CUDA_ARCH_BIN中的不支持的版本去掉即可。
由于我电脑安装了cuda11、cuda11.4、gcc8、gcc9等多个版本，编译提示
1、/usr/include/crt/host_config.h:138:2: error: #error -- unsupported GNU version! gcc versions later than 8 are not supported!
138 | #error -- unsupported GNU version! gcc versions later than 8 are not supported!
这个问题是由于nvcc版本不对导致的，手动选择指定版本目录的nvcc即可，如下图
2、gcc未找到c11plusplus类似的错误，是因为gcc和g++版本不对应导致的，参考下图配置
GPD部分
正确编译pcl后未遇到问题，一次性编译通过。
Dex-Net部分
1、github下载慢或者404问题，尝试gitee 导入项目，替换github的地址clone,即可。
2、python setup.py develop异常，没有继续 调试，后续有时间再搞；使用ros方式编译，sudo sh install.sh（sh文件中已经替换github到gitee）
3、dexnet中引用的包，autolab_core等python版本为2，请切换至3（我的电脑为ubuntu20.04）
否则会报错，下图为其中之一
4、配置realsense2的perception的launch，启动后报错如下图，解决办法，
5、所有项目在python3下支持不友好，print格式不对、import路径不对，需要一一修改
6、meshpy编译错误，
/home/song/turtlebot_ros/grasp_ws/src/dex_net/meshpy/meshpy/meshrender.cpp:3:10: fatal error: boost/python/numeric.hpp: 没有那个文件或目录
3 | #include "boost/python/numeric.hpp"
没解决，最后通过pip install meshpy解决。
8 | #include "GL/osmesa.h"
安装：sudo apt-get install libosmesa6-devmoveit deep grasp部分
moveit deep grasp处在测试阶段，并没有完善的代码，遇到的坑比较多，后面调试过程中在下面一一列出解决办法。
前言：
moveit deep grasp 依赖图，moveit task contructor是框架，融合了moveit和gpd等算法，实现抓取。
问题：
编译moveit task constructor问题
1、undefined reference to `GOMP_loop_nonmonotonic_dynamic_start@GOMP_4.5'
原因：缺少gomp.so链接导致的
解决方案：
set(EXT_LIBS libgomp.so)
target_link_libraries(point_cloud_server
${catkin_LIBRARIES}
${PROJECT_NAME}
${PCL_LIBRARIES}
${EXT_LIBS}
)
2、/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_global_init@CURL_OPENSSL_4'
/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_easy_perform@CURL_OPENSSL_4'
/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_easy_setopt@CURL_OPENSSL_4'
/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_easy_init@CURL_OPENSSL_4'
/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_easy_cleanup@CURL_OPENSSL_4'
/usr/bin/ld: /opt/ros/noetic/lib/libresource_retriever.so: undefined reference to `curl_global_cleanup@CURL_OPENSSL_4'
第一眼看到原因应该和上个问题一样，重新编译curl并且支持OpenSSL4，猜测是这个原因。
git clone songyb/curl
编译后并没有解决，最终发现因为anaconda导致环境变换引起的，卸载后上述两个现象都解决了。
3、发现一个moveit源码BUG，
/**
* \def MOVEIT_STRUCT_FORWARD
* Like MOVEIT_CLASS_FORWARD, but forward declares the type as a struct
* instead of a class.
*/
#define MOVEIT_STRUCT_FORWARD(C) \
struct C; \
MOVEIT_DECLARE_PTR(C, C) 缺少一个";" noetic版本和master分支缺少，老版本（melodic，kinetic）等有。
编译通过。
MoveIT和gpd联合规划仿真遇到的问题
1、内存溢出, ，gdb调试后发现moveit官方代码中chomp planner 报错，因此尝试更换planner到ompl规划算法；deep grasp
2、更换ompl后，第一个问题没复现，出现新的问题，
terminate called after throwing an instance of 'std::runtime_error'
what(): Property 'group': undefined
stage 'close hand': declared, but undefined
问题出现在源码BUG，https://github.com/ros-planning/moveit_task_constructor/issues/204#
替换所有的stage->properties().configureInitFrom(Stage::PARENT, { "group" });
到stage→setGroup(arm_group_name_);
可以解决。
Arm架构移植
背景
由于x86代码在台式机器上，远程点云等数据传输延时，不方便调试，因此，尝试把gpd代码移植到agx上去。
系统环境
OS:Ubuntu 18.04
ROS: melodic apt安装
PCL部分
和x86架构类似，没遇到问题，就是编译时间比较长，
OpenVino
参考安装手册
Install Intel® Distribution of OpenVINO™ toolkit for Linux* Using APT Repository — OpenVINO™ documentation
问题：xavier的处理器架构不支持，因此放弃。后面尝试采用和部署caffe
Caffe
参考安装
Caffe installation on Xavier - Jetson & Embedded Systems / Jetson AGX Xavier - NVIDIA Developer Forums
问题1： Could NOT find LMDB (missing: LMDB_INCLUDE_DIR LMDB_LIBRARIES)
解决方法：sudo apt-get install liblmdb-dev libleveldb-dev libsnappy-dev libatlas-base-dev doxygen
问题2： Found cuDNN: ver. ??? found (include: /usr/include, library: /usr/lib/aarch64-linux-gnu/libcudnn.so)
CMake Error at cmake/Cuda.cmake:227 (message):
cuDNN version >3 is required.
说明，已经安装cudnn，但是没找到版本号，手动添加版本号，#define CUDNN_MAJOR 5到文件，/usr/include/cudnn.h
问题3：
CMake Error: The following variables are used in this project, but they are set to NOTFOUND.
Please set them or make sure they are set and tested correctly in the CMake files:
CUDA_cublas_device_LIBRARY (ADVANCED)
linked by target "caffe" in directory /media/sd/soft/caffe/src/caffe
解决办法：
升级Cmake版本可以解决。
CUDA_cublas_device_LIBRARY-NOTFOUND · Issue #22 · weigao95/surfelwarp (github.com)
升级途径：https://cmake.org/files/v3.22/cmake-3.22.1-linux-aarch64.sh
问题4：Caffe installation on Xavier - Jetson & Embedded Systems / Jetson AGX Xavier - NVIDIA Developer Forums
makefile.config 配置，参考上面链接
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome!# cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1# CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1# uncomment to disable IO dependencies and corresponding data layers # USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 0 # This code is taken from https://github.com/sh1r0/caffe-android-lib # USE_HDF5 := 0# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) # You should not set this flag if you will be reading LMDBs with any # possibility of simultaneous read and write # ALLOW_LMDB_NOLOCK := 1# Uncomment if you're using OpenCV 3OPENCV_VERSION := 3# To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++# CUDA directory contains bin/ and lib/ directories that we need. CUDA_DIR := /usr/local/cuda # On Ubuntu 14.04, if cuda tools are installed via # "sudo apt-get install nvidia-cuda-toolkit" then use this instead: # CUDA_DIR := /usr# CUDA architecture setting: going with all of them. # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility. # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility. # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility. CUDA_ARCH := -gencode arch=compute_72,code=sm_72 \-gencode arch=compute_72,code=compute_72# BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := atlas # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # Leave commented to accept the defaults for your choice of BLAS # (which should work)! # BLAS_INCLUDE := /path/to/your/blas # BLAS_LIB := /path/to/your/blas# Homebrew puts openblas in a directory that is not on the standard search path # BLAS_INCLUDE := $(shell brew --prefix openblas)/include # BLAS_LIB := $(shell brew --prefix openblas)/lib# This is required only if you will compile the matlab interface. # MATLAB directory should contain the mex binary in /bin. # MATLAB_DIR := /usr/local # MATLAB_DIR := /Applications/MATLAB_R2012b.app# NOTE: this is required only if you will compile the python interface. # We need to be able to find Python.h and numpy/arrayobject.h. PYTHON_INCLUDE := /usr/include/python2.7 \/usr/lib/python2.7/dist-packages/numpy/core/include # Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it's in root. # ANACONDA_HOME := $(HOME)/anaconda # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \# $(ANACONDA_HOME)/include/python2.7 \# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include# Uncomment to use Python 3 (default is Python 2) # PYTHON_LIBRARIES := boost_python3 python3.5m # PYTHON_INCLUDE := /usr/include/python3.5m \ # /usr/lib/python3.5/dist-packages/numpy/core/include# We need to be able to find libpythonX.X.so or .dylib. PYTHON_LIB := /usr/lib # PYTHON_LIB := $(ANACONDA_HOME)/lib# Homebrew installs numpy in a non standard path (keg only) # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include # PYTHON_LIB += $(shell brew --prefix numpy)/lib# Uncomment to support layers written in Python (will link against Python libs) # WITH_PYTHON_LAYER := 1# Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies # INCLUDE_DIRS += $(shell brew --prefix)/include # LIBRARY_DIRS += $(shell brew --prefix)/lib# NCCL acceleration switch (uncomment to build with NCCL) # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0) # USE_NCCL := 1# Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1# N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1# The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0# enable pretty build (comment to see full commands) Q ?= @ |
问题5：error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope int cv_read_flag = (is_color ? CV_LOA
.原来是说，旧版的声明已经没了，但是我可以在constants_c.h 这个头文件中获得，那么根据他的操作来吧。
同样，在使用了CV_LOAD_IMAGE_COLOR这个定义的c文件下，添加头文件：
#include "opencv2/imgcodecs/legacy/constants_c.h"
————————————————
版权声明：本文为CSDN博主「wx_polish」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：SLAM十四讲，第七章程序ch7报错， error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope_XXX的博客-CSDN博客_cv_load_image_color
GPD部分
问题1：c++: error: unrecognized command line option ‘-msse4.2’
解决办法cmake将
you have to disable -msse option in Cmake configuration. ARM processor of JETSON does not support SSE instructions.
问题2：不支持sse编译
c++ - using cmake to make a library without sse support (windows version) - Stack Overflow
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