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… 
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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
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References

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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.
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TLDR
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Inverse Depth Parametrization for Monocular SLAM
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Appearance-only SLAM at large scale with FAB-MAP 2.0
TLDR
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