falfab / orb_slam_cuda

ORB-SLAM 2 with nVidia CUDA call to make it faster

ORB-SLAM2 4 Matlab/Simulink

We provide a script build.sh to build the Thirdparty libraries and ORB-SLAM2. Please make sure you have installed all required dependencies (see section 2). Execute:

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

KITTI Dataset

1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php http://kitti.is.tue.mpg.de/kitti/data_odometry_calib.zip http://kitti.is.tue.mpg.de/kitti/data_odometry_gray.zip

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

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

#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 The system localizes the camera in the map (which is no longer updated), using relocalization if needed.