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• Documented ORB SLAM2 ros implementation.Maintainer status: maintained

• Maintainer: Lennart Haller

• Author:

• License: GPLv3

• Source: git -Initiative/orb_slam_2_ros.git (branch: master)

• Package LinksCode API

• Srv API

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• Dependencies (13)catkin

• cv_bridge

• dynamic_reconfigure

• image_transport

• message_generation

• message_runtime

• roscpp

• rospy

• sensor_msgs

• std_msgs

• tf

• tf2_geometry_msgs

• tf2_ros

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• Package Summary Documented ORB SLAM2 ros implementation.Maintainer status: maintained

• Maintainer: Lennart Haller

• Author:

• License: GPLv3

• Source: git -Initiative/orb_slam_2_ros.git (branch: master)

• ContentsORB-SLAM2

• ORB-SLAM2 ROS nodeFeatures

• ROS parameters, topics and servicesParameters

• Published topics

• Subscribed topics

• Services

ORB-SLAM2ORB-SLAM2 Authors: RaulMur-Artal,JuanD.Tardos, J.M.M.Montiel and DorianGalvez-Lopez (DBoW2). The original implementation can be found here. ORB-SLAM2 ROS nodeThis is a ROS implementation of the ORB-SLAM2 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. This implementation removes the Pangolin dependency, and the original viewer. All data I/O is handled via ROS topics. For vizualization you can use RViz. Features- Full ROS compatibility - Supports a lot of cameras out of the box, such as the Intel RealSense family. See the run section for a list - Data I/O via ROS topics - Parameters can be set with the rqt_reconfigure gui during runtime - Very quick startup through considerably sped up vocab file loading - Full Map save and load functionality ROS parameters, topics and servicesParametersThere are three types of parameters right now: static- and dynamic ros parameters and camera settings from the config file. The static parameters are send to the ROS parameter server at startup and are not supposed to change. They are set in the launch files which are located at ros/launch. The parameters are: - load_map: Bool. If set to true, the node will try to load the map provided with map_file at startup. - map_file: String. The name of the file the map is saved at. - settings_file: String. The location of config file mentioned above. - voc_file:String. The location of config vocanulary file mentioned above. - publish_pose: Bool. If a PoseStamped message should be published. Even if this is false the tf will still be published. - publish_pointcloud: Bool. If the pointcloud containing all key points (the map) should be published. - pointcloud_frame_id: String. The Frame id of the Pointcloud/map. - camera_frame_id: String. The Frame id of the camera position. Dynamic parameters can be changed at runtime. Either by updating them directly via the command line or by using rqt_reconfigure which is the recommended way. The parameters are: - localize_only: Bool. Toggle from/to only localization. The SLAM will then no longer add no new points to the map. - reset_map: Bool. Set to true to erase the map and start new. After reset the parameter will automatically update back to false. - min_num_kf_in_map: Int. Number of key frames a map has to have to not get reset after tracking is lost. Finally, the intrinsic camera calibration parameters along with some hyperparameters can be found in the specific yaml files in orb_slam2/config. Published topicsThe following topics are being published and subscribed to by the nodes: - All nodes publish (given the settings) a PointCloud2 containing all key points of the map. - Live image from the camera containing the currently found key points and a status text. - A tf from the pointcloud frame id to the camera frame id (the position). Subscribed topics- The mono node subscribes to /camera/image_raw for the input image. - The RGBD node subscribes to /camera/rgb/image_raw for the RGB image and - /camera/depth_registered/image_raw for the depth information. - The stereo node subscribes to image_left/image_color_rect and - image_right/image_color_rect for corresponding images. ServicesAll nodes offer the possibility to save the map via the service node_type/save_map. So the save_map services are: - /orb_slam2_rgbd/save_map - /orb_slam2_mono/save_map - /orb_slam2_stereo/save_map Wiki: orb_slam2_ros (last edited 2019-07-25 14:34:35 by LennartHaller)

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We may have multiple downloads for few games when different versions are available.Also, we try to upload manuals and extra documentation when possible. If you have additional files to contribute or have the game in another language, please contact us!

You can compare the optimized camera trajectory with the ground truth to evaluate the accuracy. The downloaded data contains a groundtruth.txt file that stores the ground truth of camera pose of each frame. The data has been saved in the form of a MAT-file. You can also calculate the root-mean-square-error (RMSE) of trajectory estimates.

A simple solution is to download a suitable version of vision_opencv from -perception/vis.... Then extract the contents (containing some packages cv_bridge image_geometry opencv_tests vision_opencv) to a folder called vision_opencv of your src folder in your catkin workspace.

To get the KITTI test sequences, download the odometry data set (grayscale, 22 GB). After registering, you receive a download link to the data_odometry_gray.zip file. When you unpack it, you get the following structure:

You can download now to test the game, give a try to the creator mode (the feature we are most proud of) so you can test the moves, styles and IA for your own fighters. In the demo version you can store up to 8 created fighters and test them agains the IA as an spectator.

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The data used in this example are from the UTIAS Long-Term Localization and Mapping Dataset provided by University of Toronto Institute for Aerospace Studies. You can download the data to a directory using a web browser or by running the following code:

You can compare the optimized camera trajectory with the ground truth to evaluate the accuracy of the solution. The downloaded data contains a gps.txt file that stores the GPS location for each frame. You can convert the GPS location from geographic to local Cartesian coordinates using latlon2local (Automated Driving Toolbox) from Automated Driving Toolbox or geodetic2enu (Mapping Toolbox) from Mapping Toolbox. In this example, you can simply load the converted GPS data from an M-file. 041b061a72