This is the stella_vslam documentation.

NOTE: This is a community fork of xdspacelab/openvslam. It was created to continue active development of OpenVSLAM on Jan 31, 2021. The original repository is no longer available. Please read README.md in stella_vslam.

Contents

Overview

stella_vslam is a monocular, stereo, and RGBD visual SLAM system. The notable features are:

It is compatible with various type of camera models and can be easily customized for other camera models.
Created maps can be stored and loaded, then stella_vslam can localize new images based on the prebuilt maps.
The system is fully modular. It is designed by encapsulating several functions in separated components with easy-to-understand APIs.
We provided some code snippets to understand the core functionalities of this system.

stella_vslam is based on an indirect SLAM algorithm with sparse features, such as ORB-SLAM, ProSLAM, and UcoSLAM. One of the noteworthy features of stella_vslam is that the system can deal with various type of camera models, such as perspective, fisheye, and equirectangular. If needed, users can implement extra camera models (e.g. dual fisheye, catadioptric) with ease. For example, visual SLAM algorithm using equirectangular camera models (e.g. RICOH THETA series, insta360 series, etc) is shown above.

Some code snippets to understand the core functionalities of the system are provided. You can employ these snippets for in your own programs. Please see the *.cc files in ./example directory or check Simple Tutorial and Example.

Also, some examples to run stella_vslam on ROS framework are provided. Please check ROS Package.

Please contact us via GitHub issues if you have any questions or notice any bugs about the software.

Installation

Please see Installation chapter.

The instructions for Docker users are also provided.

Tutorial

Please see Simple Tutorial.

A sample ORB vocabulary file can be downloaded from here.

Sample datasets are also provided at here.

If you would like to run visual SLAM with standard benchmarking datasets (e.g. KITTI Odometry dataset), please see SLAM with standard datasets.

Reference

Raúl Mur-Artal, J. M. M. Montiel, and Juan D. Tardós. 2015. ORB-SLAM: a Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics 31, 5 (2015), 1147–1163.
Raúl Mur-Artal and Juan D. Tardós. 2017. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics 33, 5 (2017), 1255–1262.
Dominik Schlegel, Mirco Colosi, and Giorgio Grisetti. 2018. ProSLAM: Graph SLAM from a Programmer’s Perspective. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA). 1–9.
Rafael Muñoz-Salinas and Rafael Medina Carnicer. 2019. UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers. arXiv:1902.03729.
Mapillary AB. 2019. OpenSfM. https://github.com/mapillary/OpenSfM.
Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, and Wolfram Burgard. 2010. A Tutorial on Graph-Based SLAM. IEEE Transactions on Intelligent Transportation SystemsMagazine 2, 4 (2010), 31–43.
Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, and Wolfram Burgard. 2011. g2o: A general framework for graph optimization. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA). 3607–3613.

Installation

Source code

The source code can be viewed from this GitHub repository.

Cloning the repository:

git clone --recursive https://github.com/stella-cv/stella_vslam.git

If you are Windows 10 user, please install the dependencies and stella_vslam with SocketViewer support on Windows Subsystem for Linux (WSL).

Docker systems can be used instead of preparing the dependencies manually.

Dependencies

stella_vslam requires a C++11-compliant compiler. It relies on several open-source libraries as shown below.

Requirements for stella_vslam

Eigen : version 3.3.0 or later.
g2o : Please use the latest release. Tested on commit ID ed40a5b.
SuiteSparse : Required by g2o.
FBoW : Please use the custom version of FBoW released in https://github.com/stella-cv/FBoW.
yaml-cpp : version 0.6.0 or later.
OpenCV : version 3.3.1 or later.

Note

OpenCV with GUI support is necessary for using the built-in viewer (Pangolin Viewer). OpenCV with video support is necessary if you plan on using video files (e.g. .mp4) as inputs. If your CPU has many cores, it is recommended to enable TBB.

Requirements for PangolinViewer

We provided an OpenGL-based simple viewer.
This viewer is implemented with Pangolin. Thus, we call it PangolinViewer.
Please install the following dependencies if you plan on using PangolinViewer.

Pangolin : Please use the latest release. Tested on commit ID eab3d34.
GLEW : Required by Pangolin.

Note

If Pangolin version 0.7 or higher, C++17 is required.

Requirements for SocketViewer

We provided an WebGL-based simple viewer running on web browsers.
The SLAM systems publish the map and the frame to the server implemented with Node.js via WebSocket. Thus, we call it SocketViewer.
Please install the following dependencies if you plan on using SocketViewer.

socket.io-client-cpp : Please use the custom version of socket.io-client-cpp released in https://github.com/shinsumicco/socket.io-client-cpp.
Protobuf : version 3 or later.

The following libraries are the dependencies for the server.

Node.js : version 6 or later.
npm : Tested on version 3.5.2.

Recommended

backward-cpp : Used for stack-trace logger.

Prerequisites for Unix

Note

If your PC is frozen during the build, please reduce the number of parallel compile jobs when executing make (e.g. make -j2).

Installing for Linux

Tested for Ubuntu 18.04.

Install the dependencies via apt.

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 ffmpeg libavcodec-dev libavformat-dev libavutil-dev libswscale-dev libavresample-dev libtbb-dev
# eigen dependencies
apt install -y gfortran
# backward-cpp dependencies (optional)
apt install -y binutils-dev
# other dependencies
apt install -y libyaml-cpp-dev libgflags-dev sqlite3 libsqlite3-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_12.x | sudo -E bash -
apt install -y nodejs

Download and install Eigen from source.

cd /path/to/working/dir
wget -q https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.tar.bz2
tar xf eigen-3.3.7.tar.bz2 && rm -rf eigen-3.3.7.tar.bz2
cd eigen-3.3.7
mkdir -p build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    ..
make -j4 && make install

Download, build and install OpenCV from source.

cd /path/to/working/dir
# Download OpenCV
wget -q https://github.com/opencv/opencv/archive/4.5.5.zip
unzip -q 4.5.5.zip && rm -rf 4.5.5.zip
# Download aruco module (optional)
wget -q https://github.com/opencv/opencv_contrib/archive/refs/tags/4.5.5.zip -O opencv_contrib-4.5.5.zip
unzip -q opencv_contrib-4.5.5.zip && rm -rf opencv_contrib-4.5.5.zip
mkdir -p extra && mv opencv_contrib-4.5.5/modules/aruco extra
rm -rf opencv_contrib-4.5.5
# Build and install OpenCV
cd opencv-4.5.5
mkdir -p build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_DOCS=OFF \
    -DBUILD_EXAMPLES=OFF \
    -DBUILD_JASPER=OFF \
    -DBUILD_OPENEXR=OFF \
    -DBUILD_PERF_TESTS=OFF \
    -DBUILD_TESTS=OFF \
    -DBUILD_PROTOBUF=OFF \
    -DBUILD_opencv_apps=OFF \
    -DBUILD_opencv_dnn=OFF \
    -DBUILD_opencv_ml=OFF \
    -DBUILD_opencv_python_bindings_generator=OFF \
    -DENABLE_CXX11=ON \
    -DENABLE_FAST_MATH=ON \
    -DWITH_EIGEN=ON \
    -DWITH_FFMPEG=ON \
    -DWITH_TBB=ON \
    -DWITH_OPENMP=ON \
    -DOPENCV_EXTRA_MODULES_PATH=/tmp/extra \
    ..
make -j4 && make install

Jump to Common Installation Instructions for the next step.

Installing for macOS

Tested for macOS High Sierra.

Install the dependencies via brew.

brew update
# basic dependencies
brew install pkg-config cmake git
# g2o dependencies
brew install suite-sparse
# OpenCV dependencies and OpenCV
brew install eigen ffmpeg opencv
# other dependencies
brew install yaml-cpp glog gflags sqlite3

# (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

Jump to Common Installation Instructions for the next step.

Common Installation Instructions

Download, build and install the custom FBoW from source.

cd /path/to/working/dir
git clone https://github.com/stella-cv/FBoW.git
cd FBoW
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 ed40a5bb028566fd56a78fd7b04921b613492d6f
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_SHARED_LIBS=ON \
    -DBUILD_UNITTESTS=OFF \
    -DG2O_USE_CHOLMOD=OFF \
    -DG2O_USE_CSPARSE=ON \
    -DG2O_USE_OPENGL=OFF \
    -DG2O_USE_OPENMP=OFF \
    -DG2O_BUILD_APPS=OFF \
    -DG2O_BUILD_EXAMPLES=OFF \
    -DG2O_BUILD_LINKED_APPS=OFF \
    ..
make -j4 && make install

Download, build and install backward-cpp.

cd /path/to/working/dir
git clone https://github.com/bombela/backward-cpp.git
cd backward-cpp
git checkout 5ffb2c879ebdbea3bdb8477c671e32b1c984beaa
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    ..
make -j4 && make install

(if you plan on using PangolinViewer)

Download, build and install Pangolin from source.

cd /path/to/working/dir
git clone https://github.com/stevenlovegrove/Pangolin.git
cd Pangolin
git checkout eab3d3449a33a042b1ee7225e1b8b593b1b21e3e
mkdir build && cd build
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_EXAMPLES=OFF \
    -DBUILD_PANGOLIN_DEPTHSENSE=OFF \
    -DBUILD_PANGOLIN_FFMPEG=OFF \
    -DBUILD_PANGOLIN_LIBDC1394=OFF \
    -DBUILD_PANGOLIN_LIBJPEG=OFF \
    -DBUILD_PANGOLIN_LIBOPENEXR=OFF \
    -DBUILD_PANGOLIN_LIBPNG=OFF \
    -DBUILD_PANGOLIN_LIBTIFF=OFF \
    -DBUILD_PANGOLIN_LIBUVC=OFF \
    -DBUILD_PANGOLIN_LZ4=OFF \
    -DBUILD_PANGOLIN_OPENNI=OFF \
    -DBUILD_PANGOLIN_OPENNI2=OFF \
    -DBUILD_PANGOLIN_PLEORA=OFF \
    -DBUILD_PANGOLIN_PYTHON=OFF \
    -DBUILD_PANGOLIN_TELICAM=OFF \
    -DBUILD_PANGOLIN_UVC_MEDIAFOUNDATION=OFF \
    -DBUILD_PANGOLIN_V4L=OFF \
    -DBUILD_PANGOLIN_ZSTD=OFF \
    ..
make -j4 && make install

(if you plan on using SocketViewer)

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

cd /path/to/working/dir
git clone https://github.com/shinsumicco/socket.io-client-cpp.git
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

(if you plan on using SocketViewer)

Install Protobuf.

If you use Ubuntu 18.04 or macOS, Protobuf 3.x can be installed via apt or brew.

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

Otherwise, please download, build and install Protobuf from source.

wget -q https://github.com/google/protobuf/archive/v3.6.1.tar.gz
tar xf v3.6.1.tar.gz
cd protobuf-3.6.1
./autogen.sh
./configure \
    --prefix=/usr/local \
    --enable-static=no
make -j4
make install

Build Instructions

When building with support for PangolinViewer, please specify the following cmake options: -DUSE_PANGOLIN_VIEWER=ON and -DUSE_SOCKET_PUBLISHER=OFF.

cd /path/to/stella_vslam
mkdir build && cd build
cmake \
    -DUSE_STACK_TRACE_LOGGER=ON \
    -DCMAKE_BUILD_TYPE=RelWithDebInfo \
    -DUSE_PANGOLIN_VIEWER=ON \
    -DINSTALL_PANGOLIN_VIEWER=ON \
    -DUSE_SOCKET_PUBLISHER=OFF \
    -DBUILD_TESTS=OFF \
    -DBUILD_EXAMPLES=ON \
    ..
make -j4 && make install

When building with support for SocketViewer, please specify the following cmake options: -DUSE_PANGOLIN_VIEWER=OFF and -DUSE_SOCKET_PUBLISHER=ON.

cd /path/to/stella_vslam
mkdir build && cd build
cmake \
    -DUSE_STACK_TRACE_LOGGER=ON \
    -DCMAKE_BUILD_TYPE=RelWithDebInfo \
    -DUSE_PANGOLIN_VIEWER=OFF \
    -DUSE_SOCKET_PUBLISHER=ON \
    -DINSTALL_SOCKET_PUBLISHER=ON \
    -DBUILD_TESTS=OFF \
    -DBUILD_EXAMPLES=ON \
    ..
make -j4 && make install

After building, check to see if it was successfully built by executing ./run_kitti_slam -h.

$ ./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
--log-level arg (=info)  log level

Server Setup for SocketViewer

If you plan on using SocketViewer, please setup the environment for the server with npm.

$ cd /path/to/stella_vslam/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

Then, launch the server with node app.js.

$ cd /path/to/stella_vslam/viewer
$ 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

After launching, please access to http://localhost:3001/ to check whether the server is correctly launched.

Note

When you try the tutotial and the examples with SocketViewer, please launch the server in the other terminal and access to it with the web browser in advance.

Simple Tutorial

TL; DR

Note

If you use SocketViewer, please launch the server in the other terminal and access to it with the web browser in advance.

Running the following commands will give a feel for what stella_vslam can do. The later parts of this chapter explains what each of the commands do in more detail.

# at the build directory of stella_vslam ...
$ pwd
/path/to/stella_vslam/build/
$ ls
run_video_slam   lib/   ...

# download an ORB vocabulary from GitHub
curl -sL "https://github.com/stella-cv/FBoW_orb_vocab/raw/main/orb_vocab.fbow" -o orb_vocab.fbow

# 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.fbow -m ./aist_living_lab_1/video.mp4 -c ../example/aist/equirectangular.yaml --frame-skip 3 --no-sleep --map-db-out map.msg
# click the [Terminate] button to close the viewer
# you can find map.msg in the current directory

# run localization
./run_video_slam --disable-mapping -v ./orb_vocab.fbow -m ./aist_living_lab_2/video.mp4 -c ../example/aist/equirectangular.yaml --frame-skip 3 --no-sleep --map-db-in map.msg

Sample Datasets

You can use stella_vslam with various video datasets. If you want to run stella_vslam with standard benchmarking detasets, please see this section.

Start by downloading some datasets you like.

Equirectangular Datasets

name	camera model	length	Google Drive	Baidu Wangpan
aist_entrance_hall_1	equirectangular (mono)	0:54	link	link (Pass: r7r4)
aist_entrance_hall_2	equirectangular (mono)	0:54	link	link (Pass: 4qma)
aist_factory_A_1	equirectangular (mono)	1:55	link	link (Pass: yy2u)
aist_factory_A_2	equirectangular (mono)	1:54	link	link (Pass: 9vey)
aist_factory_B_1	equirectangular (mono)	1:04	link	link (Pass: gpec)
aist_factory_B_2	equirectangular (mono)	1:34	link	link (Pass: ugrx)
aist_living_lab_1	equirectangular (mono)	2:16	link	link (Pass: 434m)
aist_living_lab_2	equirectangular (mono)	1:47	link	link (Pass: 549f)
aist_living_lab_3	equirectangular (mono)	2:06	link	link (Pass: cc2p)
aist_stairs_A_1	equirectangular (mono)	2:27	link	link (Pass: ncdr)
aist_stairs_B_1	equirectangular (mono)	2:55	link	link (Pass: xr5t)
aist_store_1	equirectangular (mono)	1:12	link	link (Pass: 47vq)
aist_store_2	equirectangular (mono)	1:44	link	link (Pass: xt8u)
aist_store_3	equirectangular (mono)	1:18	link	link (Pass: kghc)
ALL	equirectangular (mono)		link	link (Pass: vsv7)

Fisheye Datasets

name	camera model	length	Google Drive	Baidu Wangpan
aist_entrance_hall_1	fisheye (mono)	1:05	link	link (Pass: u86e)
aist_entrance_hall_2	fisheye (mono)	1:06	link	link (Pass: 9iyc)
aist_entrance_hall_3	fisheye (mono)	1:23	link	link (Pass: qaqc)
aist_entrance_hall_4	fisheye (mono)	1:27	link	link (Pass: em43)
aist_living_lab_1	fisheye (mono)	1:20	link	link (Pass: wcw4)
aist_living_lab_2	fisheye (mono)	2:26	link	link (Pass: dxns)
aist_living_lab_3	fisheye (mono)	3:43	link	link (Pass: 7n4q)
nu_eng2_corridor_1	fisheye (mono)	2:56	link	link (Pass: 71ws)
nu_eng2_corridor_2	fisheye (mono)	2:45	link	link (Pass: yrtj)
nu_eng2_corridor_3	fisheye (mono)	2:04	link	link (Pass: btpj)
ALL	fisheye (mono)		link	link (Pass: gumj)

After downloading and uncompressing a zip file, you will find a video file and a config file (old format) under the uncompressed directory.

$ ls dataset_name_X/
config.yaml  video.mp4

You can put the dataset in any directory where you have access to.

Additionally, please download a vocabulary file for FBoW from here.

For the rest of this chapter, we will use aist_living_lab_1 and aist_living_lab_2 datasets for our example.

Tracking and Mapping

Here we should know how to run SLAM and create a map database file with aist_living_lab_1 dataset. You can use ./run_video_slam to run SLAM with the video file.

# at the build directory of stella_vslam
$ ls
...
run_video_slam
...
$ ./run_video_slam -h
Allowed options:
  -h, --help               produce help message
  -v, --vocab arg          vocabulary file path
  -m, --video arg          video file path
  -c, --config arg         config file path
  --mask arg               mask image 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
  --log-level arg (=info)  log level

Execute the following command to run SLAM. The paths should be changed accordingly.

$ ./run_video_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -c /path/to/stella_vslam/example/aist/equirectangular.yaml \
    -m /path/to/aist_living_lab_1/video.mp4 \
    --frame-skip 3 \
    --map-db-out aist_living_lab_1_map.msg

The frame viewer and map viewer should launch as well. If the two viewers are not launching correctly, check if you launched the command with the appropriate paths.

[2019-05-20 17:52:41.677] [I] config file loaded: /path/to/stella_vslam/example/aist/equirectangular.yaml
  ___               __   _____ _      _   __  __
 / _ \ _ __  ___ _ _\ \ / / __| |    /_\ |  \/  |
| (_) | '_ \/ -_) ' \\ V /\__ \ |__ / _ \| |\/| |
 \___/| .__/\___|_||_|\_/ |___/____/_/ \_\_|  |_|
      |_|

Copyright (C) 2019,
National Institute of Advanced Industrial Science and Technology (AIST)
All rights reserved.
For the changes after forking,
Copyright (C) 2022, stella-cv, All rights reserved.

This is free software,
and you are welcome to redistribute it under certain conditions.
See the LICENSE file.

Camera Configuration:
- name: RICOH THETA S 960
- setup: Monocular
- fps: 30
- cols: 1920
- rows: 960
- color: RGB
- model: Equirectangular
ORB Configuration:
- number of keypoints: 2000
- scale factor: 1.2
- number of levels: 8
- initial fast threshold: 20
- minimum fast threshold: 7
- edge threshold: 19
- patch size: 31
- half patch size: 15
- mask rectangles:
  - [0, 1, 0, 0.1]
  - [0, 1, 0.84, 1]
  - [0, 0.2, 0.7, 1]
  - [0.8, 1, 0.7, 1]
Tracking Configuration:

[2019-05-20 17:52:41.678] [I] loading ORB vocabulary: /path/to/orb_vocab/orb_vocab.fbow
[2019-05-20 17:52:42.037] [I] startup SLAM system
[2019-05-20 17:52:42.038] [I] start local mapper
[2019-05-20 17:52:42.038] [I] start loop closer
[2019-05-20 17:52:42.395] [I] initialization succeeded with E
[2019-05-20 17:52:42.424] [I] new map created with 191 points: frame 0 - frame 2
[2019-05-20 17:53:39.092] [I] detect loop: keyframe 36 - keyframe 139
[2019-05-20 17:53:39.094] [I] pause local mapper
[2019-05-20 17:53:39.303] [I] resume local mapper
[2019-05-20 17:53:39.303] [I] start loop bundle adjustment
[2019-05-20 17:53:40.186] [I] finish loop bundle adjustment
[2019-05-20 17:53:40.186] [I] updating map with pose propagation
[2019-05-20 17:53:40.194] [I] pause local mapper
[2019-05-20 17:53:40.199] [I] resume local mapper
[2019-05-20 17:53:40.199] [I] updated map
[2019-05-20 17:55:36.218] [I] shutdown SLAM system
[2019-05-20 17:55:36.218] [I] encoding 1 camera(s) to store
[2019-05-20 17:55:36.218] [I] encoding 301 keyframes to store
[2019-05-20 17:55:37.906] [I] encoding 19900 landmarks to store
[2019-05-20 17:55:38.819] [I] save the MessagePack file of database to aist_living_lab_1_map.msg
median tracking time: 0.045391[s]
mean tracking time: 0.0472221[s]
[2019-05-20 17:55:40.087] [I] clear BoW database
[2019-05-20 17:55:40.284] [I] clear map database

Please click the Terminate button to close the viewer.

After terminating, you will find a map database file aist_living_lab_1_map.msg.

$ ls
...
aist_living_lab_1_map.msg
...

The format of map database files is MessagePack, so you can reuse created maps for any third-party applications other than stella_vslam.

Localization

In this section, we will localize the frames in aist_living_lab_2 dataset using the created map file aist_living_lab_1_map.msg. You can use ./run_video_slam with --map-db-in aist_living_lab_1_map.msg --disable-mapping to run localization. Execute the following command to start localization. The paths should be changed accordingly.

$ ./run_video_slam --disable-mapping \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -c /path/to/stella_vslam/example/aist/equirectangular.yaml \
    -m /path/to/aist_living_lab_2/video.mp4 \
    --frame-skip 3 \
    --map-db-in aist_living_lab_1_map.msg

The frame viewer and map viewer should launch as well. If the two viewers are not launching correctly, check if you launched the command with the appropriate paths.

You can see if the current frame is being localized, based on the prebuild map.

[2019-05-20 17:58:54.728] [I] config file loaded: /path/to/stella_vslam/example/aist/equirectangular.yaml
  ___               __   _____ _      _   __  __
 / _ \ _ __  ___ _ _\ \ / / __| |    /_\ |  \/  |
| (_) | '_ \/ -_) ' \\ V /\__ \ |__ / _ \| |\/| |
 \___/| .__/\___|_||_|\_/ |___/____/_/ \_\_|  |_|
      |_|

Copyright (C) 2019,
National Institute of Advanced Industrial Science and Technology (AIST)
All rights reserved.
For the changes after forking,
Copyright (C) 2022, stella-cv, All rights reserved.

This is free software,
and you are welcome to redistribute it under certain conditions.
See the LICENSE file.

Camera Configuration:
- name: RICOH THETA S 960
- setup: Monocular
- fps: 30
- cols: 1920
- rows: 960
- color: RGB
- model: Equirectangular
ORB Configuration:
- number of keypoints: 2000
- scale factor: 1.2
- number of levels: 8
- initial fast threshold: 20
- minimum fast threshold: 7
- edge threshold: 19
- patch size: 31
- half patch size: 15
- mask rectangles:
  - [0, 1, 0, 0.1]
  - [0, 1, 0.84, 1]
  - [0, 0.2, 0.7, 1]
  - [0.8, 1, 0.7, 1]
Tracking Configuration:

[2019-05-20 17:58:54.729] [I] loading ORB vocabulary: /path/to/orb_vocab/orb_vocab.fbow
[2019-05-20 17:58:55.083] [I] clear map database
[2019-05-20 17:58:55.083] [I] clear BoW database
[2019-05-20 17:58:55.083] [I] load the MessagePack file of database from aist_living_lab_1_map.msg
[2019-05-20 17:58:57.832] [I] decoding 1 camera(s) to load
[2019-05-20 17:58:57.832] [I] load the tracking camera "RICOH THETA S 960" from JSON
[2019-05-20 17:58:58.204] [I] decoding 301 keyframes to load
[2019-05-20 17:59:02.013] [I] decoding 19900 landmarks to load
[2019-05-20 17:59:02.036] [I] registering essential graph
[2019-05-20 17:59:02.564] [I] registering keyframe-landmark association
[2019-05-20 17:59:03.161] [I] updating covisibility graph
[2019-05-20 17:59:03.341] [I] updating landmark geometry
[2019-05-20 17:59:04.189] [I] startup SLAM system
[2019-05-20 17:59:04.190] [I] start local mapper
[2019-05-20 17:59:04.191] [I] start loop closer
[2019-05-20 17:59:04.195] [I] pause local mapper
[2019-05-20 17:59:04.424] [I] relocalization succeeded
[2019-05-20 18:01:12.387] [I] shutdown SLAM system
median tracking time: 0.0370831[s]
mean tracking time: 0.0384683[s]
[2019-05-20 18:01:12.390] [I] clear BoW database
[2019-05-20 18:01:12.574] [I] clear map database

If you set the --mapping option, the mapping module is enabled to extend the prebuild map.

Running on Docker

Instructions for PangolinViewer

Dockerfile.desktop can be used for easy installation. This chapter provides instructions on building and running examples with PangolinViewer support using Docker.

The instructions are tested on Ubuntu 18.04 and 20.04. Docker for Mac are NOT supported due to OpenGL forwarding.

Note

If you’re using Ubuntu, there are easy setup scripts in scripts/ubuntu. see scripts/ubuntu/README.md

If you plan on using a machine with NVIDIA graphics card(s), please use nvidia-docker2 and the version 390 or later of NVIDIA driver. These examples depend on X11 forwarding with OpenGL for visualization.

If the viewer cannot be lanched at all or you are using macOS, please install the dependencies manually or use the docker images for SocketViewer.

Building Docker Image

Execute the following commands:

git clone --recursive https://github.com/stella-cv/stella_vslam.git
cd stella_vslam
docker build -t stella_vslam-desktop -f Dockerfile.desktop .

You can accelerate the build of the docker image with --build-arg NUM_THREADS=<number of parallel builds> option. For example:

# building the docker image with four threads
docker build -t stella_vslam-desktop -f Dockerfile.desktop . --build-arg NUM_THREADS=`expr $(nproc) - 1`

Starting Docker Container

In order to enable X11 forwarding, supplemental options (-e DISPLAY=$DISPLAY and -v /tmp/.X11-unix/:/tmp/.X11-unix:ro) are needed for docker run.

# before launching the container, allow display access from local users
xhost +local:
# launch the container
docker run -it --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix:ro stella_vslam-desktop

Note

Additional option --runtime=nvidia is needed if you use NVIDIA graphics card(s). If you’re using Docker with Native GPU Support then the options are --gpus all. Please see here for more details.

After launching the container, the shell interface will be launched in the docker container.

root@ddad048b5fff:/stella_vslam/build# ls
lib                     run_image_slam          run_video_slam
run_euroc_slam          run_kitti_slam          run_tum_slam

See Tutorial to run SLAM examples in the container.

Note

If the viewer does not work, please install the dependencies manually on your host machine or use the docker images for SocketViewer instead.

If you need to access to any files and directories on a host machine from the container, bind directories between the host and the container.

Instructions for SocketViewer

Dockerfile.socket and viewer/Dockerfile can be used for easy installation. This chapter provides instructions on building and running examples with SocketViewer support using Docker.

Building Docker Images

Docker Image of stella_vslam

Execute the following commands:

cd /path/to/stella_vslam
docker build -t stella_vslam-socket -f Dockerfile.socket .

You can accelerate the build of the docker image with --build-arg NUM_THREADS=<number of parallel builds> option. For example:

# building the docker image with four threads
docker build -t stella_vslam-socket -f Dockerfile.socket . --build-arg NUM_THREADS=`expr $(nproc) - 1`

Docker Image of Server

Execute the following commands:

cd /path/to/stella_vslam
cd viewer
docker build -t stella_vslam-viewer .

Starting Docker Containers

On Linux

Launch the server container and access to it with the web browser in advance. Please specify --net=host in order to share the network with the host machine.

$ docker run --rm -it --name stella_vslam-viewer --net=host stella_vslam-viewer
WebSocket: listening on *:3000
HTTP server: listening on *:3001

After launching, access to http://localhost:3001/ with the web browser.

Next, launch the container of stella_vslam. The shell interface will be launched in the docker container.

$ docker run --rm -it --name stella_vslam-socket --net=host stella_vslam-socket
root@hostname:/stella_vslam/build#

See Tutorial to run SLAM examples in the container.

If you need to access to any files and directories on a host machine from the container, bind directories between the host and the container.

On macOS

Launch the server container and access to it with the web browser in advance. Please specify -p 3001:3001 for port-forwarding.

$ docker run --rm -it --name stella_vslam-viewer -p 3001:3001 stella_vslam-viewer
WebSocket: listening on *:3000
HTTP server: listening on *:3001

After launching, access to http://localhost:3001/ with the web browser.

Then, inspect the container’s IP address and append the SocketPublisher.server_uri entry to the YAML config file of stella_vslam.

# inspect the server's IP address
$ docker inspect stella_vslam-viewer | grep -m 1 \"IPAddress\" | sed 's/ //g' | sed 's/,//g'
"IPAddress": "172.17.0.2"

# config file of stella_vslam

...

#============================#
# SocketPublisher Parameters #
#============================#

# append this entry
SocketPublisher.server_uri: "http://172.17.0.2:3000"

Next, launch the container of stella_vslam. The shell interface will be launched in the docker container.

$ docker run --rm -it --name stella_vslam-socket stella_vslam-socket
root@hostname:/stella_vslam/build#

See Tutorial to run SLAM examples in the container.

Please don’t forget to append SocketPublisher.server_uri entry to the config.yaml if you use the downloaded datasets in the tutorial.

If you need to access to any files and directories on a host machine from the container, bind directories between the host and the container.

Bind of Directories

If you need to access to any files and directories on a host machine from the container, bind directories between the host and the container using --volume or --mount option. (See the docker documentataion.)

For example:

# launch a container of stella_vslam-desktop with --volume option
$ docker run -it --rm --runtime=nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix:ro \
    --volume /path/to/dataset/dir/:/dataset:ro \
    --volume /path/to/vocab/dir:/vocab:ro \
    stella_vslam-desktop
# dataset/ and vocab/ are found at the root directory in the container
root@0c0c9f115d74:/# ls /
...   dataset/   vocab/   ...

# launch a container of stella_vslam-socket with --volume option
$ docker run --rm -it --name stella_vslam-socket --net=host \
    --volume /path/to/dataset/dir/:/dataset:ro \
    --volume /path/to/vocab/dir:/vocab:ro \
    stella_vslam-socket
# dataset/ and vocab/ are found at the root directory in the container
root@0c0c9f115d74:/# ls /
...   dataset/   vocab/   ...

Running on ROS

We provide ROS and ROS2 package examples to help you run stella_vslam on ROS framework.

ROS Package

Installation

Requirements

ROS : noetic is recommended. (If you have built OpenCV (3.3.1 or later) manually, you can use melodic or later.)
stella_vslam
image_transport : Required by this ROS package examples.
cv_bridge : Please build it with the same version of OpenCV used in stella_vslam.

Prerequisites

Tested for Ubuntu 18.04.

Please install the following dependencies.

ROS : Please follow Installation of ROS.
stella_vslam : Please follow Installation of stella_vslam.

Note

Please build stella_vslam with PangolinViewer or SocketViewer if you plan on using it for the examples.

Install the dependencies via apt.

apt update -y
apt install ros-${ROS_DISTRO}-image-transport

Download the source of cv_bridge.

mkdir -p ~/catkin_ws/src
git clone --branch ${ROS_DISTRO} --depth 1 https://github.com/ros-perception/vision_opencv.git
cp -r vision_opencv/cv_bridge ~/catkin_ws/src
rm -rf vision_opencv

Build Instructions

When building with support for PangolinViewer, please specify the following cmake options: -DUSE_PANGOLIN_VIEWER=ON and -DUSE_SOCKET_PUBLISHER=OFF as described in build of stella_vslam. stella_vslam and stella_vslam_ros need to be built with the same options.

cd ~/catkin_ws/src
git clone --recursive --branch ros --depth 1 https://github.com/stella-cv/stella_vslam_ros.git
cd ~/catkin_ws
catkin_make -DUSE_PANGOLIN_VIEWER=ON -DUSE_SOCKET_PUBLISHER=OFF

Examples

Run the core program required for ROS-based system in advance.

roscore

Note

Please leave the roscore run.

Publisher

Publish Images by a video

rosrun image_publisher image_publisher ./aist_living_lab_1/video.mp4 /image_raw:=/camera/image_raw

Publish Images of a USB Camera

For using a standard USB camera for visual SLAM or localization.

apt install ros-${ROS_DISTRO}-usb-cam

rosparam set usb_cam/pixel_format yuyv
rosrun usb_cam usb_cam_node

Republish the ROS topic to /camera/image_raw.

rosrun image_transport republish \
    raw in:=/usb_cam/image_raw raw out:=/camera/image_raw

Subscriber

Subscribers continually receive images. Please execute one of the following command snippets in the new terminal.

Note

Option arguments are the same as the examples of stella_vslam.

Tracking and Mapping

We provide an example snippet for visual SLAM. The source code is placed at stella_vslam_ros/src/run_slam.cc.

source ~/catkin_ws/devel/setup.bash
rosrun stella_vslam_ros run_slam \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-out /path/to/map.msg

Localization

We provide an example snippet for localization based on a prebuilt map. The source code is placed at stella_vslam_ros/src/run_slam.cc.

source ~/catkin_ws/devel/setup.bash
rosrun stella_vslam_ros run_slam \
    --disable-mapping \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-in /path/to/map.msg

ROS2 Package

Installation

Requirements

ROS2 : foxy or later.
stella_vslam
image_common : Required by this ROS package examples.
vision_opencv : Please build it with the same version of OpenCV used in stella_vslam.
image_tools : An optional requirement to use USB cameras.

Prerequisites

Tested for Ubuntu 20.04.

Please install the following dependencies.

ROS2 : Please follow Installation of ROS2.
stella_vslam : Please follow Installation of stella_vslam.

Note

Please build stella_vslam with PangolinViewer or SocketViewer if you plan on using it for the examples.

Download repositories of image_common and vision_opencv.

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src
git clone -b ${ROS_DISTRO} --single-branch https://github.com/ros-perception/image_common.git
git clone -b ros2 --single-branch https://github.com/ros-perception/vision_opencv.git

For using USB cam as a image source, donload a repository of demos and pick image_tools module.

cd ~/ros2_ws
git clone https://github.com/ros2/demos.git
cp -r demos/image_tools src/
rm -rf demos

Build Instructions

When building with support for PangolinViewer, please specify the following cmake options: -DUSE_PANGOLIN_VIEWER=ON and -DUSE_SOCKET_PUBLISHER=OFF as described in build of stella_vslam. stella_vslam and stella_vslam_ros need to be built with the same options.

cd ~/ros2_ws/src
git clone --recursive --branch ros2 --depth 1 https://github.com/stella-cv/stella_vslam_ros.git
cd ~/ros2_ws
colcon build --symlink-install --cmake-args -DUSE_PANGOLIN_VIEWER=ON -DUSE_SOCKET_PUBLISHER=OFF

Examples

Publisher

Publish Images by a video

ros2 run image_publisher image_publisher_node ./aist_living_lab_1/video.mp4 --ros-args --remap /image_raw:=/camera/image_raw

Publish Images Captured by a USB Camera

For using a standard USB camera for visual SLAM or localization.

ros2 run image_tools cam2image

Republish the ROS topic to /camera/image_raw.

ros2 run image_transport republish \
    raw in:=image raw out:=/camera/image_raw

Subscriber

Subscribers continually receive images. Please execute one of the following command snippets in the new terminal.

Note

Option arguments are the same as the examples of stella_vslam.

Tracking and Mapping

We provide an example snippet for visual SLAM. The source code is placed at stella_vslam_ros/src/run_slam.cc.

source ~/ros2_ws/install/setup.bash
ros2 run stella_vslam_ros run_slam \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-out /path/to/map.msg

Localization

We provide an example snippet for localization based on a prebuilt map. The source code is placed at stella_vslam_ros/src/run_slam.cc.

source ~/ros2_ws/install/setup.bash
ros2 run stella_vslam_ros run_slam \
    --disable-mapping \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-in /path/to/map.msg

Example

We provided example code snippets for running stella_vslam with variety of datasets.

SLAM with Video Files

We provide an example snippet for using video files (e.g. .mp4) for visual SLAM. The source code is placed at ./example/run_video_slam.cc.

The camera that captures the video file must be calibrated. Create a config file (.yaml) according to the camera parameters.

We provided a vocabulary file for FBoW at here.

You can create a map database file by running one of the run_****_slam executables with --map-db-out map_file_name.msg option.

SLAM with Image Sequences

We provided an example snippet for using image sequences for visual SLAM. The source code is placed at ./example/run_image_slam.cc.

The camera that captures the video file must be calibrated. Create a config file (.yaml) according to the camera parameters.

We provided a vocabulary file for FBoW at here.

You can create a map database file by running one of the run_****_slam executables with --map-db-out map_file_name.msg option.

SLAM with Standard Datasets

KITTI Odometry dataset

KITTI Odometry dataset is a benchmarking dataset for monocular and stereo visual odometry and lidar odometry that is captured from car-mounted devices. We provided an example source code for running monocular and stereo visual SLAM with this dataset. The source code is placed at ./example/run_kitti_slam.cc.

Start by downloading the dataset from here. Download the grayscale set (data_odometry_gray.zip).

After downloading and uncompressing it, you will find several sequences under the sequences/ directory.

$ ls sequences/
00  01  02  03  04  05  06  07  08  09  10  11  12  13  14  15  16  17  18  19  20  21

In addition, download a vocabulary file for FBoW from here.

A configuration file for each sequence is contained under ./example/kitti/.

If you built examples with Pangolin Viewer support, a map viewer and frame viewer will be launced right after executing the following command.

# at the build directory of stella_vslam
# monocular SLAM with sequence 00
$ ./run_kitti_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/KITTI/Odometry/sequences/00/ \
    -c ../example/kitti/KITTI_mono_00-02.yaml
# stereo SLAM with sequence 05
$ ./run_kitti_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/KITTI/Odometry/sequences/05/ \
    -c ../example/kitti/KITTI_stereo_04-12.yaml

EuRoC MAV dataset

EuRoC MAV dataset is a benchmarking dataset for monocular and stereo visual odometry that is captured from drone-mounted devices. We provide an example source code for running monocular and stereo visual SLAM with this dataset. The source code is placed at ./example/run_euroc_slam.cc.

Start by downloading the dataset from here. Download the .zip file of a dataset you plan on using.

After downloading and uncompressing it, you will find several directories under the mav0/ directory.

$ ls mav0/
body.yaml  cam0  cam1  imu0  leica0  state_groundtruth_estimate0

In addition, download a vocabulary file for FBoW from here.

We provided the two config files for EuRoC, ./example/euroc/EuRoC_mono.yaml for monocular and ./example/euroc/EuRoC_stereo.yaml for stereo.

If you have built examples with Pangolin Viewer support, a map viewer and frame viewer will be launched right after executing the following command.

# at the build directory of stella_vslam
# monocular SLAM with any EuRoC sequence
$ ./run_euroc_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/EuRoC/MAV/mav0/ \
    -c ../example/euroc/EuRoC_mono.yaml
# stereo SLAM with any EuRoC sequence
$ ./run_euroc_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/EuRoC/MAV/mav0/ \
    -c ../example/euroc/EuRoC_stereo.yaml

TUM RGBD dataset

TUM RGBD dataset is a benchmarking dataset fcontaining RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. The source code is placed at ./example/run_tum_rgbd_slam.cc.

Start by downloading the various dataset from here. One of many example datasets can be found from here. Download the .tgz file of a dataset you plan on using.

After downloading and uncompressing it, you will find two directories and few text files under the rgbd_dataset_freiburg3_calibration_rgb_depth/ directory.

$ ls rgbd_dataset_freiburg3_calibration_rgb_depth
accelerometer.txt  depth  depth.txt  groundtruth.txt  rgb  rgb.txt

If you would like to preprocess dataset then you can usee tool from here.

In addition, download a vocabulary file for FBoW from here.

We provided the config files for RGBD dataset at, ./example/tum_rgbd.

For above specific example we shall use two config files, ./example/tum_rgbd/TUM_RGBD_mono_3.yaml for monocular and ./example/tum_rgbd/TUM_RGBD_rgbd_3.yaml for RGBD.

Tracking and Mapping

# at the build directory of stella_vslam
# monocular SLAM with rgbd_dataset_freiburg3_calibration_rgb_depth
$ ./run_tum_rgbd_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/rgbd_dataset_freiburg3_calibration_rgb_depth/ \
    -c ../example/tum_rgbd/TUM_RGBD_mono_3.yaml \
    --no-sleep \
    --auto-term \
    --map-db-out fr3_slam_mono.msg

# RGBD SLAM with rgbd_dataset_freiburg3_calibration_rgb_depth
$ ./run_tum_rgbd_slam \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/rgbd_dataset_freiburg3_calibration_rgb_depth/ \
    -c ../example/tum_rgbd/TUM_RGBD_rgbd_3.yaml \
    --no-sleep \
    --auto-term \
    --map-db-out fr3_slam_rgbd.msg

Localization

# at the build directory of stella_vslam
# monocular localization with rgbd_dataset_freiburg3_calibration_rgb_depth
$ ./run_tum_rgbd_slam --disable-mapping \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/rgbd_dataset_freiburg3_calibration_rgb_depth/ \
    -c ../example/tum_rgbd/TUM_RGBD_mono_3.yaml \
    --no-sleep \
    --auto-term \
    --map-db-in fr3_slam_mono.msg

# RGBD SLAM with rgbd_dataset_freiburg3_calibration_rgb_depth
$ ./run_tum_rgbd_slam --disable-mapping \
    -v /path/to/orb_vocab/orb_vocab.fbow \
    -d /path/to/rgbd_dataset_freiburg3_calibration_rgb_depth/ \
    -c ../example/tum_rgbd/TUM_RGBD_rgbd_3.yaml \
    --no-sleep \
    --auto-term \
    --map-db-in fr3_slam_rgbd.msg

SLAM with UVC camera

Tracking and Mapping

We provided an example snippet for using a UVC camera, which is often called a webcam, for visual SLAM. The source code is placed at ./example/run_camera_slam.cc.

Please specify the camera number you want to use by -n option.
The camera must be calibrated. Create a config file (.yaml) according to the camera parameters.
You can scale input images to the performance of your machine by -s option. Please modify the config accordingly.
We provided a vocabulary file for FBoW at here.

Parameters

System

Name	Description
map_format	msgpack or sqlite3
min_num_shared_lms	minimum number of shared landmarks for covisibility graph connection. Keyframes that exceed the threshold are considered to share a sufficient number of landmarks and are used for local BA, loop detection and graph optimization.

Camera

Name	Description
name	It is used by the camera database to identify the camera.
setup	monocular, stereo, RGBD
model	perspective, fisheye, equirectangular, radial_division (note: If you want to use stereo_rectifier, you need to specify perspective.)
fx, fy	Focal length (pixel)
cx, cy	Principal point (pixel)
k1, k2, p1, p2, k3	Distortion parameters for perspective camera. When using StereoRectifier, there is no distortion after stereo rectification.
k1, k2, k3, k4	Distortion parameters for fisheye camera
distortion	Distortion parameters for radial_division camera
fps	Framerate of input images
cols, rows	Resolution (pixel)
color_order	Gray, RGB, RGBA, BGR, BGRA
focal_x_baseline	For stereo cameras, it is the value of the baseline between the left and right cameras multiplied by the focal length fx. For RGBD cameras, if the measurement method is stereo, set it based on its baseline. If the measurement method is other than that, set the appropriate value based on the relationship between depth accuracy and baseline.
depth_threshold	The ratio used to determine the depth threshold.

Feature

Name	Description
name	name of ORB feature extraction model (id for saving)
scale_factor	Scale of the image pyramid
num_levels	Number of levels of in the image pyramid
ini_fast_threshold	FAST threshold for try first
min_fast_threshold	FAST threshold for try second time

Preprocessing

Name	Description
min_size	Size of node occupied by one feature point. The larger this value, the fewer feature points are extracted.
depthmap_factor	The ratio used to convert depth image pixel values to distance.

Tracking

Name	Description
reloc_distance_threshold	Maximum distance threshold (in meters) where close keyframes could be found when doing a relocalization by pose.
reloc_angle_threshold	Maximum angle threshold (in radians) between given pose and close keyframes when doing a relocalization by pose.
enable_auto_relocalization	If true, automatically try to relocalize when lost.
use_robust_matcher_for_relocalization_request	If true, use robust_matcher for relocalization request.
max_num_local_keyfrms	Max number of local keyframes for tracking.
backend	g2o or gtsam

Mapping

Name	Description
baseline_dist_thr_ratio	For two frames of baseline below the threshold, no triangulation will be performed. In the monocular case, the scale is indefinite, so relative values are recommended. Either baseline_dist_thr or this one should be specified. If not specified, baseline_dist_thr_ratio will be used.
baseline_dist_thr	For two frames of baseline below the threshold, no triangulation will be performed.
redundant_obs_ratio_thr
observed_ratio_thr
num_reliable_keyfrms
enable_interruption_of_landmark_generation	If true, enable interruption of landmark generation
enable_interruption_before_local_BA	If true, enable interruption before local BA
backend	g2o or gtsam

StereoRectifier

Name	Description
model	camera model type before rectification. The option is perspective or fisheye. (note: If you want to use fisheye model for stereo_rectifier, you need to specify Camera::model to perspective.)
K_left, K_right	Intrinsic parameters. The 3x3 matrix are written in row-major order.
D_left, D_right	Distortion parameters. The 5 parameters are k1, k2, p1, p2, k3.
R_left, R_right	Stereo-recitification parameters. The 3x3 matrix are written in row-major order.

Initializer

Name	Description
num_ransac_iterations	max number of iterations of RANSAC (only for monocular initializer)
min_num_valid_pts	min number of valid pts (It should be greater than or equal to min_num_triangulated_)
min_num_triangulated_pts	Minimum number of triangulated points
parallax_deg_threshold	min parallax (only for monocular initializer)
reprojection_error_threshold	reprojection error threshold (only for monocular initializer)
num_ba_iterations	max number of iterations of BA (only for monocular initializer)
scaling_factor	initial scaling factor (only for monocular initializer)
use_fixed_seed	Use fixed random seed for RANSAC if true

Relocalizer

Name	Description
bow_match_lowe_ratio
proj_match_lowe_ratio
min_num_bow_matches
min_num_valid_obs

KeyframeInserter

Name	Description
max_interval	max interval to insert keyframe
min_interval
max_distance
enough_lms_thr
lms_ratio_thr_almost_all_lms_are_tracked	Threshold at which we consider that we are tracking almost all landmarks. Ratio-threshold of “the number of 3D points observed in the current frame” / “that of 3D points observed in the last keyframe”
lms_ratio_thr_view_changed	Threshold at which we consider the view to have changed. Ratio-threshold of “the number of 3D points observed in the current frame” / “that of 3D points observed in the last keyframe”

PangolinViewer

Name	Description
keyframe_size
keyframe_line_width
graph_line_width
point_size
camera_size
camera_line_width
menu_width
viewpoint_x, viewpoint_y, viewpoint_z, viewpoint_f

SocketPublisher

Name	Description
emitting_interval
image_quality
server_uri
max_num_keyframes	Limit the number of keyframes transferred at one time. This avoids disconnections when loading large maps.
max_num_landmarks	Limit the number of landmarks transferred at one time. This avoids disconnections when loading large maps.
publish_points	If true, pointcloud transfer is enabled. The default is true. Pointcloud transfer is slow, so disabling pointcloud transfer may be useful to improve performance of SocketViewer.

LoopDetector

Name	Description
enabled	flag which indicates the loop detector is enabled or not
num_final_matches_threshold	the threshold of the number of mutual matches after the Sim3 estimation
min_continuity	the threshold of the continuity of continuously detected keyframe set
reject_by_graph_distance	If true, reject by distance on essential graph
loop_min_distance_on_graph	Minimum distance to allow for loop candidates
top_n_covisibilities_to_search	Top n covisibilities to search (0 means disabled)
num_matches_thr	Minimum number of matches to allow for loop candidates
num_matches_thr_brute_force	Minimum number of matches to allow for loop candidates after brute force matching. (0 means disabled)
num_optimized_inliers_thr	Minimum number of matches to allow for loop candidates after optimization by transform_optimizer
backend	g2o or gtsam

MarkerModel

Name	Description
type	Only “aruco” is a valid value
width	Physical size of marker
marker_size	4, 5, 6, 7. See https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html.
max_markers	50, 100, 250, 1000. See https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html.

Relocalization

What is Relocalization? Why it is needed?

In Visual SLAM, the robot/camera explores its environment while

estimates its location using the map and the last location as prior information (Tracking), and simultaneously
update the map (the database that records landmarks) of environment (Mapping).

Relocalization module can estimate the location without using any prior information other than the map (with the high cost of computation). This is useful when the previous location cannot be used as prior information, for example when tracking fails.

Steps in Relocalization

Acquire relocalization candidate keyframes (Used as a reference keyframe for relocalization)
Compute matching points for each candidate by using BoW tree matcher
Discard the candidate if the number of 2D-3D matches is less than the threshold
Setup an PnP solver with the current 2D-3D matches
Estimate the camera pose using EPnP (+ RANSAC)
Apply pose optimizer
Apply projection match to increase 2D-3D matches
Re-apply the pose optimizer
Apply projection match again if the number of the observations is less than the threshold
Apply projection match again, then set the 2D-3D matches
Discard if the number of the observations is less than the threshold and do the pose estimation again
If the number of observation is greater than threshold succeed in relocalization

See the details on how to run the relocalization at here.

Trouble Shooting

For building

stella_vslam terminates abnormaly soon after launching or optimization with g2o.

Please configure and rebuild g2o and stella_vslam with -DBUILD_WITH_MARCH_NATIVE=OFF option for cmake.

For SLAM

Citation of original version of OpenVSLAM (xdspacelab/openvslam)

@inproceedings{openvslam2019,
  author = {Sumikura, Shinya and Shibuya, Mikiya and Sakurada, Ken},
  title = {{OpenVSLAM: A Versatile Visual SLAM Framework}},
  booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},
  series = {MM '19},
  year = {2019},
  isbn = {978-1-4503-6889-6},
  location = {Nice, France},
  pages = {2292--2295},
  numpages = {4},
  url = {http://doi.acm.org/10.1145/3343031.3350539},
  doi = {10.1145/3343031.3350539},
  acmid = {3350539},
  publisher = {ACM},
  address = {New York, NY, USA}
}