ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

@article{MurArtal2017ORBSLAM2AO,
  title={ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras},
  author={Raul Mur-Artal and Juan D. Tard{\'o}s},
  journal={IEEE Transactions on Robotics},
  year={2017},
  volume={33},
  pages={1255-1262}
}
We present 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. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows… 

Figures and Tables from this paper

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
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM
TLDR
A novel, fast direct BA formulation is presented which is implemented in a real-time dense RGB-D SLAM algorithm, and the proposed algorithm outperforms all other evaluated SLAM methods.
A Robust and Accurate Simultaneous Localization and Mapping System for RGB-D Cameras
TLDR
This paper presents a feature-based simultaneous localization and mapping system for RGB-D cameras that operates in real time, in indoor environments, and achieves better localization accuracy and robustness than ORB-SLAM2.
Benchmark of Visual SLAM Algorithms: ORB-SLAM2 vs RTAB-Map*
TLDR
This works deals with a benchmark of two well-known visual Simultaneous Localization and Mapping algorithms: ORB-SLAM2 proposed by Mur-Atal & al in 2015 and RTAB-Map proposed by [8]; both implemented taking into account a monocular, stereo and RGB-D camera.
Accurate and Robust Monocular SLAM with Omnidirectional Cameras
TLDR
An improved monocular visual SLAM system by using omnidirectional cameras that extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view.
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.
Semantic Object and Plane SLAM for RGB-D Cameras
TLDR
This work takes advantage of state of the art object detectors and a robust ICP method to build a map made up of objects providing semantic information as well as extra constraints to original point-based SLAM system, and introduces plane landmarks into SLAM framework to reduce drift.
3OFRR-SLAM: Visual SLAM with 3D-Assisting Optical Flow and Refined-RANSAC
TLDR
A lightweight monocular SLAM system called 3OFRR-SLAM, which is precise, fast, and achieves real-time performance on CPU and mobile phones, and a novel Refined-RANSAC, improving the accuracy of camera pose estimation without taking much extra time cost is proposed.
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
TLDR
This paper extends and improve upon a state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model and compares the robustness of the proposed method to a single camera version of the SLAM system.
HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications
TLDR
This paper deals simultaneously with the trajectory estimation and map reconstruction by means of a stereo-calibrated vision system evolving in a large-scale unknown environment and optimizes the execution time of the VSLAM framework while preserving its localization accuracy.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 25 REFERENCES
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
TLDR
A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation.
Large-Scale 6-DOF SLAM With Stereo-in-Hand
TLDR
A system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor, which accommodates both monocular and stereo.
Large-scale direct SLAM with stereo cameras
TLDR
A novel Large-Scale Direct SLAM algorithm for stereo cameras (Stereo LSD-SLAM) that runs in real-time at high frame rate on standard CPUs, capable of handling aggressive brightness changes between frames - greatly improving the performance in realistic settings.
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.
Real-time large-scale dense RGB-D SLAM with volumetric fusion
TLDR
This paper presents a new simultaneous localization and mapping (SLAM) system capable of producing high-quality globally consistent surface reconstructions over hundreds of meters in real time with only a low-cost commodity RGB-D sensor and shows that the system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.
Visual-Inertial Monocular SLAM With Map Reuse
TLDR
This letter presents a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas.
Dense visual SLAM for RGB-D cameras
TLDR
This paper proposes a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels, and proposes an entropy-based similarity measure for keyframe selection and loop closure detection.
Stereo parallel tracking and mapping for robot localization
This paper describes a visual SLAM system based on stereo cameras and focused on real-time localization for mobile robots. To achieve this, it heavily exploits the parallel nature of the SLAM
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
...
1
2
3
...