ORB-SLAM is a versatile and accurate SLAM solution for Monocular, Stereo and RGB-D cameras. It is able to compute
in real-time the camera trajectory and a sparse 3D reconstruction of the scene in
a wide variety of environments, ranging from small hand-held sequences of a desk to a car driven around several city blocks. It is able to
close large loops and perform global relocalisation in real-time and
from wide baselines. It includes an automatic and robust initialization from
planar and non-planar scenes. Demostrating videos, code and related publications are shown below.
ORB-SLAM and ORB-SLAM2 are released under a GPLv3 license.
For a closed-source version for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.
We are currently able to perform semi-dense reconstructions using the accurately localised stream of keyframes from ORB-SLAM. Check our RSS 2015 paper [2]. See the video below running in real-time in an intel core i7-4700MQ. No GPU acceleration was used. This module has not been released yet.
Monocular ORB-SLAM
(select the HD option in youtube)
Dataset: KITTI (Odometry benchmark). Sequence 00.
- Scene: large scale outdoors with multiple loops.
- Motion: car
Dataset: KITTI (Odometry benchmark). Sequence 05.
- Scene: large scale outdoors with multiple loops.
- Motion: car
Dataset: TUM RGB-D Benchmark . fr3_long_office_household.
- Scene: desk with a loop and relative scale changes
- Motion: hand-held
Dataset: TUM RGB-D Benchmark . fr3_walking_halfsphere.
- Scene: desk with people moving around
- Motion: hand-held
[3]
Raúl Mur-Artal and Juan D. Tardós. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM. Robotics: Science and Systems. Rome, Italy, July 2015. [pdf] [poster]
[4]
Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM: Tracking and Mapping Recognizable Features. Robotics: Science and Systems (RSS) Workshop on Multi VIew Geometry in RObotics (MVIGRO), Berkeley, USA, July 2014. Oral presentation [pdf]
[5]
Raúl Mur-Artal and Juan D. Tardós. Fast Relocalisation and Loop Closing in Keyframe-Based SLAM. IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, June 2014. [pdf]
