Ubuntu20.04安裝運行DynaSLAM

一、安裝Anaconda

二、相關依賴庫安裝?

1、boost安裝

2、Eigen 3安裝

3、opencv安裝?

4、Pangolin安裝

三、配置Mask_RCNN環境

四、DynaSLAM編譯

五、DynaSLAM運行

一、安裝Anaconda

打開以下鏈接：

Index of /

下載和自己系統匹配的安裝包。這里下載的是Anaconda3-2024.02-1-Linux-x86_64.sh。

在下載安裝包的目錄下打開終端，執行以下命令進行安裝。

bash Anaconda3-2024.02-1-Linux-x86_64.sh

出現以下畫面，按提示進行回車：

一直按回車，或按q直接跳過，直到出現“Do you accept the license terms”, 輸入“yes” 。

默認位置已經足夠，按ENTER確認安裝位置。

出現是否要運行conda init，輸入yes。

出現以下畫面，Anaconda安裝成功：

安裝好后，source一下環境或者重新打開一個終端。

source ~/.bashrc

會多一個（base）的前綴。

附：如果希望 conda 的基礎環境在啟動終端時不被激活，將?auto_activate_base?參數設置為 false：

conda config --set auto_activate_base false

后面想要再進入conda的base環境，只需要使用conda指令激活：

conda activate base

二、相關依賴庫安裝?

1、boost安裝

?通過以下鏈接下載boost1.71：

https://archives.boost.io/release/1.71.0/source/boost_1_71_0.tar.gz

將下載的源碼解壓縮至home下，在boost目錄下打開終端，依次執行以下命令，等待安裝完成即可：

sudo ./bootstrap.sh
sudo ./b2 install

2、Eigen 3安裝

在home下打開終端，終端執行以下命令下載Eigen3源碼：

git clone https://github.com/eigenteam/eigen-git-mirror

?然后依次執行以下命令進行安裝：

cd eigen-git-mirror
mkdir build && cd build
cmake ..
sudo make install

3、opencv安裝?

版本一定要對應，不然編譯過程中會報各種錯誤。

通過以下鏈接下載opencv3.4.5：

https://github.com/opencv/opencv/archive/refs/tags/3.4.5.tar.gz

終端執行以下命令，安裝所需的依賴項：

sudo apt-get install build-essential libgtk2.0-dev libjpeg-dev  libtiff5-dev libopenexr-dev libtbb-dev

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libgtk-3-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev pkg-config

在opencv目錄下打開終端，執行以下命令編譯安裝：?

mkdir build && cd build
cmake ..
sudo make -j4
sudo make install

終端執行以下命令，修改/etc/ld.so.conf文件：

sudo gedit /etc/ld.so.conf

在打開的文件中加上一行?include /usr/local/lib，/usr/local是opencv的默認安裝路徑，這樣告訴系統以后去lib目錄下找opencv的庫文件。

include /usr/local/lib

執行以下，命令使得conf生效：?

sudo ldconfig

終端執行以下命令，修改bash.bashrc文件：

sudo gedit /etc/bash.bashrc

在文件末尾加上以下內容：?

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH

?然后source使得bash生效：

source /etc/bash.bashrc

輸入以下命令查看opencv版本信息：?

pkg-config opencv --modversion

顯示版本，表示安裝成功！
?

4、Pangolin安裝

Pangolin一定要v0.5版本的，其他版本會報錯。

由于我這里之前安裝過Pangolin，因此需要卸載之前的版本。

（1）刪除庫和頭文件

cd Pangolin/build
make clean
sudo make uninstall

（2）刪除源代碼

cd ../..
sudo rm -r Pangolin

（3）刪除殘留文件夾

sudo updatedb
locate pangolin
sudo rm -r /usr/local/include/pangolin

通過以下鏈接下載Pangolinv0.5源碼：

?https://github.com/stevenlovegrove/Pangolin/archive/refs/tags/v0.5.tar.gz

將其解壓縮到主目錄home下，打開Pangolin文件夾，打開終端執行以下命令：

mkdir build && cd build
cmake ..
sudo make -j4
sudo make install

安裝時會出現報錯，因此需要對文件進行修改：

在/Pangolin/CMakeModules/FindFFMPEG.cmake中63,64行

        sizeof(AVFormatContext::max_analyze_duration2);}" HAVE_FFMPEG_MAX_ANALYZE_DURATION2

換成

        sizeof(AVFormatContext::max_analyze_duration);}" HAVE_FFMPEG_MAX_ANALYZE_DURATION

/Pangolin/src/video/drivers/ffmpeg.cpp中第37行 namespace pangolin上面加上

#define CODEC_FLAG_GLOBAL_HEADER AV_CODEC_FLAG_GLOBAL_HEADER

第78,79行

TEST_PIX_FMT_RETURN(XVMC_MPEG2_MC); 
TEST_PIX_FMT_RETURN(XVMC_MPEG2_IDCT);

改為

#ifdef FF_API_XVMCTEST_PIX_FMT_RETURN(XVMC_MPEG2_MC);TEST_PIX_FMT_RETURN(XVMC_MPEG2_IDCT);
#endif

第101-105行

    TEST_PIX_FMT_RETURN(VDPAU_H264);TEST_PIX_FMT_RETURN(VDPAU_MPEG1);TEST_PIX_FMT_RETURN(VDPAU_MPEG2);TEST_PIX_FMT_RETURN(VDPAU_WMV3);TEST_PIX_FMT_RETURN(VDPAU_VC1);

改為

#ifdef FF_API_VDPAUTEST_PIX_FMT_RETURN(VDPAU_H264);TEST_PIX_FMT_RETURN(VDPAU_MPEG1);TEST_PIX_FMT_RETURN(VDPAU_MPEG2);TEST_PIX_FMT_RETURN(VDPAU_WMV3);TEST_PIX_FMT_RETURN(VDPAU_VC1);
#endif

第127行

	TEST_PIX_FMT_RETURN(VDPAU_MPEG4);

改為

#ifdef FF_API_VDPAUTEST_PIX_FMT_RETURN(VDPAU_MPEG4);
#endif

在Pangolin/include/pangolin/video/drivers/ffmpeg.h開頭加上

#define AV_CODEC_FLAG_GLOBAL_HEADER (1 << 22)
#define CODEC_FLAG_GLOBAL_HEADER AV_CODEC_FLAG_GLOBAL_HEADER
#define AVFMT_RAWPICTURE 0x0020

更改完后，重新安裝即可。

三、配置Mask_RCNN環境

在Anaconda虛擬環境下配置，終端依次執行以下命令：

# 創建一個虛擬環境
conda create -n MaskRCNN python=2.7
conda activate MaskRCNN
# 這一步可能報錯，多嘗試幾次
pip install tensorflow==1.14.0
pip install keras==2.0.9
pip install scikit-image
pip install pycocotools

?在安裝pycocotools時報錯：

通過以下命令安裝pycocotools：

conda install -c conda-forge pycocotools

下載DynaSLAM并測試環境，終端執行以下命令下載DynaSLAM源碼：

git clone  https://github.com/BertaBescos/DynaSLAM.git

通過以下鏈接，下載mask_rcnn_coco.h5文件：

https://github.com/matterport/Mask_RCNN/releases/download/v1.0/mask_rcnn_coco.h5

然后把mask_rcnn_coco.h5文件放在DynaSLAM/src/python/文件夾下。

在DynaSLAM目錄下打開終端，然后執行以下命令（需在剛剛創建的MaskRCNN環境下）：

python src/python/Check.py

出現如下：Mask R-CNN is correctly working

則說明Mask_RCNN環境配置成功了。

四、DynaSLAM編譯

編譯之前需要對部分代碼進行修改。

（1）DynaSLAM/CMakeLists.txt中

C++11改為C++14

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O3 ")
# set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ")
# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O3 -march=native")
......................
#find_package(OpenCV 2.4.11 QUIET)
#if(NOT OpenCV_FOUND)
#    message("OpenCV > 2.4.11 not found.")
#    find_package(OpenCV 3.0 QUIET)
#    if(NOT OpenCV_FOUND)
#        message(FATAL_ERROR "OpenCV > 3.0 not found.")
#    endif()
#endif()find_package(OpenCV 3.4 QUIET)
if(NOT OpenCV_FOUND)find_package(OpenCV 2.4 QUIET)if(NOT OpenCV_FOUND)message(FATAL_ERROR "OpenCV > 2.4.x not found.")endif()
endif()
......................
set(Python_ADDITIONAL_VERSIONS "2.7")
#This is to avoid detecting python 3
find_package(PythonLibs 2.7 EXACT REQUIRED)
if (NOT PythonLibs_FOUND)message(FATAL_ERROR "PYTHON LIBS not found.")
else()message("PYTHON LIBS were found!")message("PYTHON LIBS DIRECTORY: " ${PYTHON_LIBRARY} ${PYTHON_INCLUDE_DIRS})
endif()
......................
#find_package(Eigen3 3.1.0 REQUIRED)
find_package(Eigen3 3 REQUIRED)
......................
# add_executable(mono_carla
# Examples/Monocular/mono_carla.cc)
# target_link_libraries(mono_carla ${PROJECT_NAME})

（2）DynaSLAM/Thirdparty/DBoW/CMakeLists.txt中

#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall  -O3 -march=native")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall  -O3 ")
......................
# find_package(OpenCV 3.0 QUIET)
find_package(OpenCV 3.4 QUIET)

（3）DynaSLAM/Thirdparty/g2o/CMakeLists.txt中

#SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 -march=native") 
#SET(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -O3 -march=native")
SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 ") 
SET(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -O3 ")
......................
#FIND_PACKAGE(Eigen3 3.1.0 REQUIRED)
FIND_PACKAGE(Eigen3 3 REQUIRED)

（4）DynaSLAM/include/Conversion.h

// cv::Mat toMat(const PyObject* o);cv::Mat toMat(PyObject* o);

（5）DynaSLAM/src/Conversion.cc

/*** This file is part of DynaSLAM.* Copyright (C) 2018 Berta Bescos <bbescos at unizar dot es> (University of Zaragoza)* For more information see <https://github.com/bertabescos/DynaSLAM>.**/#include "Conversion.h"
#include <iostream>namespace DynaSLAM
{static void init(){import_array();}static int failmsg(const char *fmt, ...){char str[1000];va_list ap;va_start(ap, fmt);vsnprintf(str, sizeof(str), fmt, ap);va_end(ap);PyErr_SetString(PyExc_TypeError, str);return 0;}class PyAllowThreads{public:PyAllowThreads() : _state(PyEval_SaveThread()) {}~PyAllowThreads(){PyEval_RestoreThread(_state);}private:PyThreadState *_state;};class PyEnsureGIL{public:PyEnsureGIL() : _state(PyGILState_Ensure()) {}~PyEnsureGIL(){// std::cout << "releasing"<< std::endl;PyGILState_Release(_state);}private:PyGILState_STATE _state;};using namespace cv;static PyObject *failmsgp(const char *fmt, ...){char str[1000];va_list ap;va_start(ap, fmt);vsnprintf(str, sizeof(str), fmt, ap);va_end(ap);PyErr_SetString(PyExc_TypeError, str);return 0;}class NumpyAllocator : public MatAllocator{public:
#if (CV_MAJOR_VERSION < 3)NumpyAllocator(){}~NumpyAllocator() {}void allocate(int dims, const int *sizes, int type, int *&refcount,uchar *&datastart, uchar *&data, size_t *step){// PyEnsureGIL gil;int depth = CV_MAT_DEPTH(type);int cn = CV_MAT_CN(type);const int f = (int)(sizeof(size_t) / 8);int typenum = depth == CV_8U ? NPY_UBYTE : depth == CV_8S ? NPY_BYTE: depth == CV_16U  ? NPY_USHORT: depth == CV_16S  ? NPY_SHORT: depth == CV_32S  ? NPY_INT: depth == CV_32F  ? NPY_FLOAT: depth == CV_64F  ? NPY_DOUBLE: f * NPY_ULONGLONG + (f ^ 1) * NPY_UINT;int i;npy_intp _sizes[CV_MAX_DIM + 1];for (i = 0; i < dims; i++){_sizes[i] = sizes[i];}if (cn > 1){_sizes[dims++] = cn;}PyObject *o = PyArray_SimpleNew(dims, _sizes, typenum);if (!o){CV_Error_(CV_StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));}refcount = refcountFromPyObject(o);npy_intp *_strides = PyArray_STRIDES(o);for (i = 0; i < dims - (cn > 1); i++)step[i] = (size_t)_strides[i];datastart = data = (uchar *)PyArray_DATA(o);}void deallocate(int *refcount, uchar *, uchar *){// PyEnsureGIL gil;if (!refcount)return;PyObject *o = pyObjectFromRefcount(refcount);Py_INCREF(o);Py_DECREF(o);}
#elseNumpyAllocator(){stdAllocator = Mat::getStdAllocator();}~NumpyAllocator(){}UMatData *allocate(PyObject *o, int dims, const int *sizes, int type,size_t *step) const{UMatData *u = new UMatData(this);u->data = u->origdata = (uchar *)PyArray_DATA((PyArrayObject *)o);npy_intp *_strides = PyArray_STRIDES((PyArrayObject *)o);for (int i = 0; i < dims - 1; i++)step[i] = (size_t)_strides[i];step[dims - 1] = CV_ELEM_SIZE(type);u->size = sizes[0] * step[0];u->userdata = o;return u;}UMatData *allocate(int dims0, const int *sizes, int type, void *data,size_t *step, int flags, UMatUsageFlags usageFlags) const{if (data != 0){CV_Error(Error::StsAssert, "The data should normally be NULL!");// probably this is safe to do in such extreme casereturn stdAllocator->allocate(dims0, sizes, type, data, step, flags,usageFlags);}PyEnsureGIL gil;int depth = CV_MAT_DEPTH(type);int cn = CV_MAT_CN(type);const int f = (int)(sizeof(size_t) / 8);int typenum =depth == CV_8U ? NPY_UBYTE : depth == CV_8S ? NPY_BYTE: depth == CV_16U  ? NPY_USHORT: depth == CV_16S  ? NPY_SHORT: depth == CV_32S  ? NPY_INT: depth == CV_32F  ? NPY_FLOAT: depth == CV_64F  ? NPY_DOUBLE: f * NPY_ULONGLONG + (f ^ 1) * NPY_UINT;int i, dims = dims0;cv::AutoBuffer<npy_intp> _sizes(dims + 1);for (i = 0; i < dims; i++)_sizes[i] = sizes[i];if (cn > 1)_sizes[dims++] = cn;PyObject *o = PyArray_SimpleNew(dims, _sizes, typenum);if (!o)CV_Error_(Error::StsError,("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));return allocate(o, dims0, sizes, type, step);}bool allocate(UMatData *u, int accessFlags,UMatUsageFlags usageFlags) const{return stdAllocator->allocate(u, accessFlags, usageFlags);}void deallocate(UMatData *u) const{if (u){PyEnsureGIL gil;PyObject *o = (PyObject *)u->userdata;Py_XDECREF(o);delete u;}}const MatAllocator *stdAllocator;
#endif};NumpyAllocator g_numpyAllocator;NDArrayConverter::NDArrayConverter() { init(); }void NDArrayConverter::init(){import_array();}cv::Mat NDArrayConverter::toMat(PyObject *o){cv::Mat m;if (!o || o == Py_None){if (!m.data)m.allocator = &g_numpyAllocator;}if (!PyArray_Check(o)){failmsg("toMat: Object is not a numpy array");}int typenum = PyArray_TYPE(o);int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE                     ? CV_8S: typenum == NPY_USHORT                     ? CV_16U: typenum == NPY_SHORT                      ? CV_16S: typenum == NPY_INT || typenum == NPY_LONG ? CV_32S: typenum == NPY_FLOAT                      ? CV_32F: typenum == NPY_DOUBLE                     ? CV_64F: -1;if (type < 0){failmsg("toMat: Data type = %d is not supported", typenum);}int ndims = PyArray_NDIM(o);if (ndims >= CV_MAX_DIM){failmsg("toMat: Dimensionality (=%d) is too high", ndims);}int size[CV_MAX_DIM + 1];size_t step[CV_MAX_DIM + 1], elemsize = CV_ELEM_SIZE1(type);const npy_intp *_sizes = PyArray_DIMS(o);const npy_intp *_strides = PyArray_STRIDES(o);bool transposed = false;for (int i = 0; i < ndims; i++){size[i] = (int)_sizes[i];step[i] = (size_t)_strides[i];}if (ndims == 0 || step[ndims - 1] > elemsize){size[ndims] = 1;step[ndims] = elemsize;ndims++;}if (ndims >= 2 && step[0] < step[1]){std::swap(size[0], size[1]);std::swap(step[0], step[1]);transposed = true;}if (ndims == 3 && size[2] <= CV_CN_MAX && step[1] == elemsize * size[2]){ndims--;type |= CV_MAKETYPE(0, size[2]);}if (ndims > 2){failmsg("toMat: Object has more than 2 dimensions");}m = Mat(ndims, size, type, PyArray_DATA(o), step);if (m.data){
#if (CV_MAJOR_VERSION < 3)m.refcount = refcountFromPyObject(o);m.addref(); // protect the original numpy array from deallocation// (since Mat destructor will decrement the reference counter)
#elsem.u = g_numpyAllocator.allocate(o, ndims, size, type, step);m.addref();Py_INCREF(o);// m.u->refcount = *refcountFromPyObject(o);
#endif};m.allocator = &g_numpyAllocator;if (transposed){Mat tmp;tmp.allocator = &g_numpyAllocator;transpose(m, tmp);m = tmp;}return m;}PyObject *NDArrayConverter::toNDArray(const cv::Mat &m){if (!m.data)Py_RETURN_NONE;Mat temp;Mat *p = (Mat *)&m;
#if (CV_MAJOR_VERSION < 3)if (!p->refcount || p->allocator != &g_numpyAllocator){temp.allocator = &g_numpyAllocator;m.copyTo(temp);p = &temp;}p->addref();return pyObjectFromRefcount(p->refcount);
#elseif (!p->u || p->allocator != &g_numpyAllocator){temp.allocator = &g_numpyAllocator;m.copyTo(temp);p = &temp;}// p->addref();// return pyObjectFromRefcount(&p->u->refcount);PyObject *o = (PyObject *)p->u->userdata;Py_INCREF(o);return o;
#endif}
}

?終端執行以下命令進行編譯：

conda activate MaskRCNN
cd DynaSLAM
chmod +x build.sh
./build.sh

若報出以下錯誤：?

終端執行以下命令安裝：?

sudo apt-get install python2.7-dev

若還有其他的錯誤，按照對應的包即可解決。?

安裝完后，重新編譯即可。重新編譯時把前一次編譯生成的?build文件刪除。

五、DynaSLAM運行

運行時需要先激活創建的虛擬環境：

conda activate MaskRCNN

創建dataset文件夾，將數據集文件rgbd_dataset_freiburg3_walking_xyz放在dataset下，如果不知道如何生成associations.txt關聯文件，以下這兩篇均有提到：

使用evo工具/rgbd_benchmark_tools評估TUM RGB-D數據集_orb-slam3 評測工具evo-CSDN博客

?YOLO_ORB_SLAM3編譯運行_yolo-orb-slam3-CSDN博客

在DynaSLAM目錄下打開終端，執行以下命令：

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt ./Examples/RGB-D/TUM3.yaml ./dataset/rgbd_dataset_freiburg3_walking_xyz/ ./dataset/rgbd_dataset_freiburg3_walking_xyz/associations.txt ./dataset/mask ./dataset/output

如果運行時報以下錯誤：Light Tracking not working because Tracking is not initialized...

Geometry not working.

可以將DynaSLAM/Examples/RGB-D/TUM3.yaml文件里的ORBextractor.nFeatures參數值由1000改為3000。或再檢查一下需要修改的文件內容有沒有全部修改。

運行成功畫面如下：