ORB-SLAM: A Versatile and Accurate Monocular SLAM System

@article{MurArtal2015ORBSLAMAV,
  title={ORB-SLAM: A Versatile and Accurate Monocular SLAM System},
  author={Raul Mur-Artal and J. M. M. Montiel and Juan D. Tard{\'o}s},
  journal={IEEE Transactions on Robotics},
  year={2015},
  volume={31},
  pages={1147-1163}
}
This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping… 
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
TLDR
ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities, is presented, being in most cases the most accurate SLAM solution.
ORB-SLAM2S: A Fast ORB-SLAM2 System with Sparse Optical Flow Tracking
This paper presents ORB-SLAM2S, a fast and complete simultaneous localization and mapping (SLAM) system based on ORB-SLAM2 for monocular, stereo, and RGB-D cameras. The system works, ensuring
A Monocular SLAM System with Mask Loop Closing
  • Bo Han, Li Xu
  • Computer Science
    2020 Chinese Control And Decision Conference (CCDC)
  • 2020
TLDR
Evaluation on the TUM mono VO dataset shows that MLC-SLAM achieves better performance than the state-of-the-art monocular SLAM algorithms: DSO and ORB- SLAM, in terms of accuracy, robustness, and the performance of loop closing.
Em-SLAM: a Fast and Robust Monocular SLAM Method for Embedded Systems
TLDR
Em-SLAM, a monocular SLAM method which is fast and robust in the embedded system, is presented in three stages comprising initial pose estimation, iterative pose optimization and correspondences, and mapping with nearest frame queue.
DOE-SLAM: Dynamic Object Enhanced Visual SLAM
TLDR
This work forms a novel strategy for monocular vSLAM that uses moving objects in the scene to improve accuracy, and extends ORB-SLAM2 to adapt to dynamic environments, estimating not only the camera trajectory based on background features but also foreground object motion.
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments
TLDR
PL-SLAM is proposed, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.
Robust Relocalization Based on Active Loop Closure for Real-Time Monocular SLAM
TLDR
An active loop closure based relocalization system, which enables the monocular SLAM to detect and recover from tracking failures automatically even in previously unvisited areas where no keyframe exists, and is more robust than others.
DM-SLAM: Monocular SLAM in Dynamic Environments
TLDR
A distribution and local-based RANSAC algorithm (DLRSAC) is proposed to extract static features from the dynamic scene based on awareness of the nature difference between motion and static, which is integrated into initialization of DM-SLAM.
SLAMM: Visual monocular SLAM with continuous mapping using multiple maps
TLDR
Simultaneous Localization and Multi-Mapping (SLAMM) is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor's malfunction; making it suitable for real-world applications.
Keyframe-based monocular SLAM: design, survey, and future directions
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 58 REFERENCES
LSD-SLAM: Large-Scale Direct Monocular SLAM
TLDR
A novel direct tracking method which operates on \(\mathfrak{sim}(3)\), thereby explicitly detecting scale-drift, and an elegant probabilistic solution to include the effect of noisy depth values into tracking are introduced.
Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM
TLDR
This paper presents a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art.
Improving the Agility of Keyframe-Based SLAM
TLDR
This paper presents two approaches to improving the agility of a keyframe-based SLAM system, and implements a very simple inter-frame rotation estimator to aid tracking when the camera is rapidly panning and enables a trivially simple yet effective relocalisation method.
MonoSLAM: Real-Time Single Camera SLAM
TLDR
The first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches is presented.
Scale Drift-Aware Large Scale Monocular SLAM
TLDR
This paper describes a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input and presents a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures.
Robust monocular SLAM in dynamic environments
TLDR
A novel prior-based adaptive RANSAC algorithm (PARSAC) is proposed to efficiently remove outliers even when the inlier ratio is rather low, so that the camera pose can be reliably estimated even in very challenging situations.
Parallel, real-time monocular visual odometry
TLDR
A real-time, accurate, large-scale monocular visual odometry system for real-world autonomous outdoor driving applications that addresses the challenge of robust multithreading even for scenes with large motions and rapidly changing imagery.
Double window optimisation for constant time visual SLAM
We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level
Inverse Depth Parametrization for Monocular SLAM
We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed
Appearance-only SLAM at large scale with FAB-MAP 2.0
TLDR
A new formulation of appearance-only SLAM suitable for very large scale place recognition that incorporates robustness against perceptual aliasing and substantially outperforms the standard term-frequency inverse-document-frequency (tf-idf) ranking measure.
...
1
2
3
4
5
...