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开源软件名称:ORB_SLAM2_Microservice开源软件地址:https://gitee.com/lianbo2006/ORB_SLAM2_Microservice开源软件介绍:ORB-SLAM2Authors: Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2) 13 Jan 2017: OpenCV 3 and Eigen 3.3 are now supported. 22 Dec 2016: Added AR demo (see section 7). ORB-SLAM2 is a real-time SLAM library for Monocular, Stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. We provide examples to run the SLAM system in the KITTI dataset as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset as stereo or monocular. We also provide a ROS node to process live monocular, stereo or RGB-D streams. The library can be compiled without ROS. ORB-SLAM2 provides a GUI to change between a SLAM Mode and Localization Mode, see section 9 of this document. Related Publications:[Monocular] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). PDF. [Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017. PDF. [DBoW2 Place Recognizer] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012. PDF SLAM 微服务化安装
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:/home/rain/workspace/ORB_SLAM2
运行分别在三个端口中启动三个节点,根据自己的目录调整相应的路径 $ rosrun ORB_SLAM2 LocalMapping Vocabulary/ORBvoc.bin$ rosrun ORB_SLAM2 LoopClosing Vocabulary/ORBvoc.bin$ rosrun ORB_SLAM2 Tracking Vocabulary/ORBvoc.bin Examples/RGB-D/TUM2.yaml Examples/RGB-D/rgbd_dataset_freiburg2_desk Examples/RGB-D/rgbd_dataset_freiburg2_desk/associate.txt LoopClosing
pSolver->SetRansacParameters(0.99,5,300); 中将第二个参数从20改为5,这样才能检测到闭环,同时也容易造成误匹配。
orb-slam环境配置方法指南详情见项目附件 1. LicenseORB-SLAM2 is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md. For a closed-source version of ORB-SLAM2 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es. If you use ORB-SLAM2 (Monocular) in an academic work, please cite: @article{murTRO2015, title={{ORB-SLAM}: a Versatile and Accurate Monocular {SLAM} System}, author={Mur-Artal, Ra\'ul, Montiel, J. M. M. and Tard\'os, Juan D.}, journal={IEEE Transactions on Robotics}, volume={31}, number={5}, pages={1147--1163}, doi = {10.1109/TRO.2015.2463671}, year={2015} } if you use ORB-SLAM2 (Stereo or RGB-D) in an academic work, please cite: @article{murORB2, title={{ORB-SLAM2}: an Open-Source {SLAM} System for Monocular, Stereo and {RGB-D} Cameras}, author={Mur-Artal, Ra\'ul and Tard\'os, Juan D.}, journal={IEEE Transactions on Robotics}, volume={33}, number={5}, pages={1255--1262}, doi = {10.1109/TRO.2017.2705103}, year={2017} } 2. PrerequisitesWe have tested the library in Ubuntu 12.04, 14.04 and 16.04, 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 CompilerWe use the new thread and chrono functionalities of C++11. PangolinWe use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin. OpenCVWe use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at leat 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2. Eigen3Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0. g2oWe use g2o library to perform non-linear optimizations. Download and install instructions can be found at: https://github.com/RainerKuemmerle/g2o DBoW2(Included in Thirdparty folder)We use modified versions of the DBoW2 library to perform place recognition(which are BSD) is 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. 3. Building ORB-SLAM2 library and examplesClone the repository: git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2 We provide a script cd ORB_SLAM2chmod +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 ExamplesTUM Dataset
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER KITTI Dataset
./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER EuRoC Dataset
./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 ExamplesKITTI Dataset
./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER EuRoC Dataset
./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 ExampleTUM Dataset
python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE 7. ROS ExamplesBuilding the nodes for mono, monoAR, stereo and RGB-D
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2/Examples/ROS
chmod +x build_ros.sh./build_ros.sh Running Monocular NodeFor a monocular input from topic rosrun ORB_SLAM2 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE Running Monocular Augmented Reality DemoThis 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 rosrun ORB_SLAM2 MonoAR PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE Running Stereo NodeFor a stereo input from topic 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 NodeFor an RGB-D input from topics rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE 8. Processing your own sequencesYou 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 ModesYou can change between the SLAM and Localization mode using the GUI of the map viewer. SLAM ModeThis 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 ModeThis 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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