ORB-SLAM: a Versatile and Accurate Monocular SLAM System
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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.Abstract:
This paper presents ORB-SLAM, a feature-based monocular 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, relocalization, and loop closing. 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. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.read more
Citations
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Journal ArticleDOI
Loosely-Coupled Semi-Direct Monocular SLAM
Seong Hun Lee,Javier Civera +1 more
TL;DR: A novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods and outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.
Proceedings ArticleDOI
Gaussian process estimation of odometry errors for localization and mapping
TL;DR: A novel approach to model odometry errors using Gaussian processes (GPs) is presented and it is shown that the approach enhances visual SLAM by efficiently computing image frames and effectively distributing keyframes.
Posted Content
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bruno Bodin,Harry Wagstaff,Sajad Saeedi,Luigi Nardi,Emanuele Vespa,John H Mayer,Andy Nisbet,Mikel Luján,Steve Furber,Andrew J. Davison,Paul H. J. Kelly,Michael O'Boyle +11 more
TL;DR: SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics.
Journal ArticleDOI
Development of a Human–Robot Hybrid Intelligent System Based on Brain Teleoperation and Deep Learning SLAM
TL;DR: A novel human–robot hybrid system incorporating a motor-imagery (MI)-based brain teleoperation control and a deep-learning-based active perception is developed in the simultaneous localization and mapping (SLAM) framework, which is more efficient and robust than traditional SLAM.
Journal ArticleDOI
Optimized Self-Localization for SLAM in Dynamic Scenes Using Probability Hypothesis Density Filters
TL;DR: The proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion, and generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms.
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