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SmarterEye-ORB-SLAM2

Install with SmarterEye-ORBSLAM2-SDK

If you wanna run ORB_SLAM2 with mynteye-s camera, please follow the steps:

  1. Download SmarterEye-ORBSLAM2-SDK and follow steps to install.
  2. Follow the normal procedure to install ORB_SLAM2.
  3. run examples by Infinite2 camera.

2. Prerequisites

We have tested the library in Ubuntu 16.04, and 18.04, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results.

C++11 or C++0x Compiler

We use the new thread and chrono functionalities of C++11.

Pangolin

We use Pangolin for visualization and user interface. step1: sudo apt-get install libglew-dev sudo apt-get install cmake sudo apt-get install libpython2.7-dev sudo apt-get install ffmpeg libavcodec-dev libavutil-dev libavformat-dev libswscale-dev libavdevice-dev sudo apt-get install libdc1394-22-dev libraw1394-dev sudo apt-get install libjpeg-dev libpng12-dev libtiff5-dev libopenexr-dev

step2: Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

step3: cd Pangolin mkdir build cd build cmake .. ##j后面是CPU的核心数量，适当修改，主要是为了编译加速 make -j4 sudo make install

OpenCV

We use OpenCV to manipulate images and features. step1: Dowload and install at: http://opencv.org. Required at leat 3 Tested with OpenCV 3.4.5.

step2: sudo apt-get install build-essential libgtk2.0-dev libavcodec-dev libavformat-dev libjpeg.dev libtiff4.dev libswscale-dev libjasper-dev

step3: cd opencv mkdir build cd build cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON .. make -j4 sudo make install

Eigen3

Required by g2o (see below). step1: Download and install at: http://eigen.tuxfamily.org. Required at least 3.1.0.

step2: cd eigen mkdir build cd build cmake make -j4 sudo make install

sudo cp -r /usr/local/include/eigen3/Eigen /usr/local/include

DBoW2 and g2o (Included in Thirdparty folder)

We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.

ROS (optional)

We provide some examples to process the live input of a monocular, stereo or RGB-D camera using ROS. Building these examples is optional. In case you want to use ROS, a version Hydro or newer is needed.

###using smartereye camera

Building the nodes for stereo (ROS)

  • Add the path including Examples/ROS/ORB_SLAM2 to the ROS_PACKAGE_PATH environment variable. Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2:

    export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:~/catkin_ws/src/SmarterEye-ORBSLAM2

  • Execute build_ros.sh script:

    
chmod +x build.sh
./build.
chmod +x build_ros.sh
./build_ros.sh

  • Launch ORB_SLAM2 Stereo_ROS Run camera SmarterEye-SDK

    cd <path of SmarterEye-sdk>/wrapper/ros/
make ros
source devel/setup.bash
rosrun zkhy_stereo_d stereo_publisher

Run ORB_SLAM2, open another terminal and follow the steps.

rosrun ORB_SLAM2 Stereo ./Vocabulary/ORBvoc.txt ./config/mycamera.yaml false

3. Building ORB-SLAM2 library and examples

cd ORB_SLAM2
chmod +x build.sh
./build.sh

This will create libORB_SLAM2.so at lib folder and the executables mono_tum, mono_kitti, rgbd_tum, stereo_kitti, mono_euroc and stereo_euroc in Examples folder.

4. Monocular Examples

TUM Dataset

  1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

  2. Execute the following command. Change TUMX.yaml to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change PATH_TO_SEQUENCE_FOLDERto the uncompressed sequence folder.

./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER

KITTI Dataset

  1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

  2. Execute the following command. Change KITTIX.yamlby KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.

./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER

EuRoC Dataset

  1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

  2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.

./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt 

./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt

5. Stereo Examples

KITTI Dataset

  1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

  2. Execute the following command. Change KITTIX.yamlto KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.

./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER

EuRoC Dataset

  1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

  2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

6. RGB-D Example

TUM Dataset

  1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

  2. Associate RGB images and depth images using the python script associate.py. We already provide associations for some of the sequences in Examples/RGB-D/associations/. You can generate your own associations file executing:

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
  1. Execute the following command. Change TUMX.yaml to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change PATH_TO_SEQUENCE_FOLDERto the uncompressed sequence folder. Change ASSOCIATIONS_FILE to the path to the corresponding associations file.
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

7. ROS Examples

Building the nodes for mono, monoAR, stereo and RGB-D

  1. Add the path including Examples/ROS/ORB_SLAM2 to the ROS_PACKAGE_PATH environment variable. Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2:
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2/Examples/ROS
  1. Execute build_ros.sh script:
chmod +x build_ros.sh
./build_ros.sh

Running Monocular Node

For a monocular input from topic /camera/image_raw run node ORB_SLAM2/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above.

rosrun ORB_SLAM2 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

Running Monocular Augmented Reality Demo

This is a demo of augmented reality where you can use an interface to insert virtual cubes in planar regions of the scene. The node reads images from topic /camera/image_raw.

rosrun ORB_SLAM2 MonoAR PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

Running Stereo Node

For a stereo input from topic /camera/left/image_raw and /camera/right/image_raw run node ORB_SLAM2/Stereo. You will need to provide the vocabulary file and a settings file. If you provide rectification matrices (see Examples/Stereo/EuRoC.yaml example), the node will recitify the images online, otherwise images must be pre-rectified.

rosrun ORB_SLAM2 Stereo PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION

Example: Download a rosbag (e.g. V1_01_easy.bag) from the EuRoC dataset (http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets). Open 3 tabs on the terminal and run the following command at each tab:

roscore

rosrun ORB_SLAM2 Stereo Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml true

rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw

Once ORB-SLAM2 has loaded the vocabulary, press space in the rosbag tab. Enjoy!. Note: a powerful computer is required to run the most exigent sequences of this dataset.

Running RGB_D Node

For an RGB-D input from topics /camera/rgb/image_raw and /camera/depth_registered/image_raw, run node ORB_SLAM2/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above.

rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

8. Processing your own sequences

You will need to create a settings file with the calibration of your camera. See the settings file provided for the TUM and KITTI datasets for monocular, stereo and RGB-D cameras. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered.

9. SLAM and Localization Modes

You can change between the SLAM and Localization mode using the GUI of the map viewer.

SLAM Mode

This is the default mode. The system runs in parallal three threads: Tracking, Local Mapping and Loop Closing. The system localizes the camera, builds new map and tries to close loops.

Localization Mode

This mode can be used when you have a good map of your working area. In this mode the Local Mapping and Loop Closing are deactivated. The system localizes the camera in the map (which is no longer updated), using relocalization if needed.

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