OpenVSLAM 설치부터 카메라출력까지

INDEX

Installation
- Source Code
- Linux
- macOS
- Build instruc
- Server Setup for Socket Viewer
Simple Tutorial
- Tutorial
- Sample Datasets
- Tracking and Mapping
- Locallization
Example
- SLAM with Video Files
- SLAM with Image Sequences
- SLAM with Standard Datasets
- SLAM with UVC cmaera

Installation

Git Source Code 다운로드 홈페이지

git clone https://github.com/xdspacelab/openvslam

다운받은 OpenVSLAM - master 폴더명을 openvslam으로 변경해준다.

OpenVSLAM요구사항

Eigen : version 3.3.0 or later. #선형대수 계산할때 사용하는 라이브러리
g2o : Please use the latest release. Tested on commit ID 9b41a4e. # 그래프 최적화 라이브러리
SuiteSparse : Required by g2o.
DBoW2 : Please use the custom version of DBoW2 released in https://github.com/shinsumicco/DBoW2. # 이미지 인덱싱
yaml-cpp : version 0.6.0 or later. # 데이터 직렬화 언어
OpenCV : version 3.3.1 or later. # 기본적인 영상처리

Viewer는 2가지로 원하는걸 사용하면 된다.

Pangolin Viewer
- Pangolin : 최신버전 released ID ad8b5f8
- GLEW
Sockt Viewer
- Socket.io-client-cpp
- Protobuf : version 3 or later

지원하는 OS는 Linux, macOS

Installing for Linux

Tested for Ubuntu 16.04.

(18.04.LTS버전 구동 확인)

apt update -y
apt upgrade -y --no-install-recommends
# basic dependencies
apt install -y build-essential pkg-config cmake git wget curl unzip
# g2o dependencies
apt install -y libatlas-base-dev libsuitesparse-dev
# OpenCV dependencies
apt install -y libgtk-3-dev
apt install -y ffmpeg
apt install -y libavcodec-dev libavformat-dev libavutil-dev libswscale-dev libavresample-dev
# eigen dependencies
apt install -y gfortran
# other dependencies
apt install -y libyaml-cpp-dev libgoogle-glog-dev libgflags-dev

# (if you plan on using PangolinViewer)
# Pangolin dependencies
apt install -y libglew-dev

# (if you plan on using SocketViewer)
# Protobuf dependencies
apt install -y autogen autoconf libtool
# Node.js
curl -sL https://deb.nodesource.com/setup_6.x | sudo -E bash -
apt install -y nodejs

Download and install Eigen form source.

cd /path/to/working/dir
wget -q http://bitbucket.org/eigen/eigen/get/3.3.4.tar.bz2
tar xf 3.3.4.tar.bz2
rm -rf 3.3.4.tar.bz2
cd eigen-eigen-5a0156e40feb
mkdir -p build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    ..
make -j4
make install

Download, bulid and install OpenCV form source.

(OpenCV3.4.0버전은 CUDA와 충돌이 있어 설치 할때 끄거나, 최신버전을 설치해야 한다 OpenCV 4.3.0 추천)

cd /path/to/working/dir
wget -q https://github.com/opencv/opencv/archive/4.3.0.zip
unzip -q 4.3.0.zip
rm -rf 4.3.0.zip
cd opencv-4.3.0
mkdir -p build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DENABLE_CXX11=ON \
    -DBUILD_DOCS=OFF \
    -DBUILD_EXAMPLES=OFF \
    -DBUILD_JASPER=OFF \
    -DBUILD_OPENEXR=OFF \
    -DBUILD_PERF_TESTS=OFF \
    -DBUILD_TESTS=OFF \
    -DWITH_EIGEN=ON \
    -DWITH_FFMPEG=ON \
    -DWITH_OPENMP=ON \
    ..
make -j4
make install

설치가 끝났으면 Common Installation Instructions 이동

Installing for macOS

Tested for macOS High Sierra.

(macOS Catalina 10.15.4 구동 확인)

Ubuntu 패키지 설치 툴인 apt-get install 대신 mac에서는 Home Brew의 패키지 설치 툴을 사용한다.

Install Home Brew

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

터미널에 붙여넣기 하면 설치 완료

하나씩 설치

brew update
# basic dependencies
brew install pkg-config cmake git
# g2o dependencies
brew install suite-sparse
# OpenCV dependencies and OpenCV
brew install eigen
brew install ffmpeg
brew install opencv
# other dependencies
brew install yaml-cpp glog gflags

# (if you plan on using PangolinViewer)
# Pangolin dependencies
brew install glew

# (if you plan on using SocketViewer)
# Protobuf dependencies
brew install automake autoconf libtool
# Node.js
brew install node

Common Installation Instrctions

Download, build and install the custom DBoW2.

cd /path/to/working/dir
git clone https://github.com/shinsumicco/DBoW2.git
cd DBoW2
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    ..
make -j4
make install

Download, build and install g2o.

cd /path/to/working/dir
git clone https://github.com/RainerKuemmerle/g2o.git
cd g2o
git checkout 9b41a4ea5ade8e1250b9c1b279f3a9c098811b5a
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DCMAKE_CXX_FLAGS=-std=c++11 \
    -DBUILD_SHARED_LIBS=ON \
    -DBUILD_UNITTESTS=OFF \
    -DBUILD_WITH_MARCH_NATIVE=ON \
    -DG2O_USE_CHOLMOD=OFF \
    -DG2O_USE_CSPARSE=ON \
    -DG2O_USE_OPENGL=OFF \
    -DG2O_USE_OPENMP=ON \
    ..
make -j4
make install

본인이 선택한 뷰어에맞게 설치

if you plan on using PangolinViewer

Download, build and install Pangolin form source.

cd /path/to/working/dir
git clone https://github.com/stevenlovegrove/Pangolin.git
cd Pangolin
git checkout ad8b5f83222291c51b4800d5a5873b0e90a0cf81
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    ..
make -j4
make install

if you plan on using SocketViewer

Download, build and install socket.io-client-cpp form source.

cd /path/to/working/dir
git clone https://github.com/shinsumicco/socket.io-client-cpp
cd socket.io-client-cpp
git submodule init
git submodule update
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_UNIT_TESTS=OFF \
    ..
make -j4
make install

Install Protobuf

(사용하는 os에 맞게 설치)

# for Ubuntu 18.04 (or later)
apt install -y libprotobuf-dev protobuf-compiler
# for macOS
brew install protobuf

Build Instruction

PangolineViewer

cd /path/to/openvslam
mkdir build && cd build
cmake \
    -DBUILD_WITH_MARCH_NATIVE=ON \
    -DUSE_PANGOLIN_VIEWER=ON \
    -DUSE_SOCKET_PUBLISHER=OFF \
    -DUSE_STACK_TRACE_LOGGER=ON \
    -DBOW_FRAMEWORK=DBoW2 \
    -DBUILD_TESTS=ON \
    ..
make -j4

SocketViewer

cd /path/to/openvslam
mkdir build && cd build
cmake \
    -DBUILD_WITH_MARCH_NATIVE=ON \
    -DUSE_PANGOLIN_VIEWER=OFF \
    -DUSE_SOCKET_PUBLISHER=ON \
    -DUSE_STACK_TRACE_LOGGER=ON \
    -DBOW_FRAMEWORK=DBoW2 \
    -DBUILD_TESTS=ON \
    ..
make -j4

기본적인 설치가 끝났다.

성공적인 설치를 확인하기 위해서 ./run_kitti_salm -h를 실행시켜보자

$ ./run_kitti_slam -h
Allowed options:
-h, --help             produce help message
-v, --vocab arg        vocabulary file path
-d, --data-dir arg     directory path which contains dataset
-c, --config arg       config file path
--frame-skip arg (=1)  interval of frame skip
--no-sleep             not wait for next frame in real time
--auto-term            automatically terminate the viewer
--debug                debug mode
--eval-log             store trajectory and tracking times for evaluation
-p, --map-db arg       store a map database at this path after SLAM

다음과 같이 출력되면 성공.

Server Setup for SocketViewer

$ cd /path/to/openvslam/viewer
$ ls
Dockerfile  app.js  package.json  public  views
$ npm install
added 88 packages from 60 contributors and audited 204 packages in 2.105s
found 0 vulnerabilities
$ ls
Dockerfile  app.js  node_modules  package-lock.json  package.json  public  views
$ node app.js
WebSocket: listening on *:3000
HTTP server: listening on *:3001

npm을 설치하고 app.js를 실행시키면 SocketViewer가 실행된다.

http://localhost:3001/ <- 접속해보면 SocketViewer가 다음과 같이 정상적으로 출력되면 성공 👏🏼

Simple Tutorial

OpenVSLAM에서 제공해주는 Sample Datasets을 이용해서 프로그램을 실행히켜봅시다.

하나씩 다운받아주세요

orb_vocab
aist_living_lab1
aist_living_lab2

$ pwd #경로확인
/path/to/openvslam/build/ 
$ ls
run_video_slam   run_video_localization   lib/   ...

# download an ORB vocabulary from Google Drive
FILE_ID="1wUPb328th8bUqhOk-i8xllt5mgRW4n84"
curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=${FILE_ID}" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -sLb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=${FILE_ID}" -o orb_vocab.zip
unzip orb_vocab.zip

# download a sample dataset from Google Drive
FILE_ID="1d8kADKWBptEqTF7jEVhKatBEdN7g0ikY"
curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=${FILE_ID}" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -sLb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=${FILE_ID}" -o aist_living_lab_1.zip
unzip aist_living_lab_1.zip

# download a sample dataset from Google Drive
FILE_ID="1TVf2D2QvMZPHsFoTb7HNxbXclPoFMGLX"
curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=${FILE_ID}" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -sLb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=${FILE_ID}" -o aist_living_lab_2.zip
unzip aist_living_lab_2.zip

# run tracking and mapping
./run_video_slam -v ./orb_vocab/orb_vocab.dbow2 -m ./aist_living_lab_1/video.mp4 -c ./aist_living_lab_1/config.yaml --frame-skip 3 --no-sleep --map-db map.msg
# click the [Terminate] button to close the viewer
# you can find map.msg in the current directory

# run localization
./run_video_localization -v ./orb_vocab/orb_vocab.dbow2 -m ./aist_living_lab_2/video.mp4 -c ./aist_living_lab_2/config.yaml --frame-skip 3 --no-sleep --map-db map.msg

3개의 파일을 다운을받고 압축을 풀었다면

복사해서 붙여넣고 엔터

./run_video_slam -v ./orb_vocab/orb_vocab.dbow2 -m ./aist_living_lab_1/video.mp4 -c ./aist_living_lab_1/config.yaml --frame-skip 3 --no-sleep --map-db map.msg

Installation에서 ./run_kitti_slam -h 를 출력하면 스니펫을 확인 할 수 있었죠 방금 실행시킨 명령어는 스니펫을 이용해서 Sample Datasets을 입력해서 맵핑을 한다는 명령어로 해석이 됩니다.

Sample Datasets의 영상을 맵핑까지 확인해보았습니다.

Example

SLAM with UVC camera

가지고 있는 USB Webcam 또는 FaceTime HD Camera를 활용해봅시다.

$ ./run_camera_slam  -h
Allowed options:
-h, --help            produce help message
-v, --vocab arg       vocabulary file path
-n, --number arg      camera number
-c, --config arg      config file path
--mask arg            mask image path
-s, --scale arg (=1)  scaling ratio of images
-p, --map-db arg      store a map database at this path after SLAM
--debug               debug mode

스니펫은 다음과 같이 제공되어 있고, -n 부분에 카메라 숫자를 입력해주면 되겠네요.

이제 카메라 디바이스 숫자를 확인해줘야합니다. Linux와 macOS 디바이스 확인 하는 방법이 다릅니다.

Ubuntu 18.04 LTS

$ ls -ltrh /dev/video*

macOS

$ ffmpeg -f avfoundation -list_devices true -i ""

숫자도 확인했으니 스니펫을 이용해서 출력해봅시다.

$ ./run_camera_slam –v ./orb_vocab/orb_vocab.dbow2 –n 1 –c ./aist_living_lab_1/config.yaml –map-db map.msg

-n 뒤에 1은 카메라 숫자입니다 본인 카메라 번호를 입력해주세요.

MacBook Pro - FaceTime HD Camera를 이용해서 출력해 보았습니다.

한가지 아쉬운점은 Mac에는 그래픽카드가 없어 제대로 테스트를 진행하지 못했습니다.

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