ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
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.Abstract:
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 for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.read more
Citations
More filters
Journal ArticleDOI
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
TL;DR: This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models, resulting in real-time robust operation in small and large, indoor and outdoor environments.
Proceedings ArticleDOI
Habitat: A Platform for Embodied AI Research
Manolis Savva,Jitendra Malik,Devi Parikh,Dhruv Batra,Abhishek Kadian,Oleksandr Maksymets,Yili Zhao,Erik Wijmans,Bhavana Jain,Julian Straub,Jia Liu,Vladlen Koltun +11 more
TL;DR: The comparison between learning and SLAM approaches from two recent works are revisited and evidence is found -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and the first cross-dataset generalization experiments are conducted.
Journal ArticleDOI
RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation
Mathieu Labbé,François Michaud +1 more
TL;DR: This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets, outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.
Journal ArticleDOI
Visual SLAM algorithms: a survey from 2010 to 2016
TL;DR: This paper focuses on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period and the technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.
Proceedings ArticleDOI
Probabilistic data association for semantic SLAM
TL;DR: This paper forms an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decomposes it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states.
References
More filters
Proceedings ArticleDOI
ORB: An efficient alternative to SIFT or SURF
TL;DR: This paper proposes a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise, and demonstrates through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations.
Journal ArticleDOI
Vision meets robotics: The KITTI dataset
TL;DR: A novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research, using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras and a high-precision GPS/IMU inertial navigation system.
Journal ArticleDOI
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
TL;DR: ORB-SLAM as discussed by the authors is a feature-based monocular SLAM system that operates in real time, in small and large indoor and outdoor environments, with a survival of the fittest strategy that selects the points and keyframes of the reconstruction.
Proceedings ArticleDOI
KinectFusion: Real-time dense surface mapping and tracking
Richard Newcombe,Shahram Izadi,Otmar Hilliges,David Molyneaux,David Kim,Andrew J. Davison,Pushmeet Kohi,Jamie Shotton,Steve Hodges,Andrew Fitzgibbon +9 more
TL;DR: A system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware, which fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real- time.
Proceedings ArticleDOI
A benchmark for the evaluation of RGB-D SLAM systems
TL;DR: A large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system is recorded for the evaluation of RGB-D SLAM systems.