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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Proceedings ArticleDOI
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Robust image matching via local graph structure consensus
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Carolina Raposo,Joao P. Barreto,Cristóvão Sousa,Luís S. Ribeiro,Rui B. Melo,João Pedro Oliveira,Pedro Marques,Fernando Fonseca,David Barrett +8 more
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Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry
Ling Yonggen,Linchao Bao,Zequn Jie,Zhu Fengming,Ziyang Li,Shanmin Tang,Yongsheng Liu,Wei Liu,Tong Zhang +8 more
TL;DR: This work proposes a nonlinear optimization-based monocular visual inertial odometry (VIO) with varying camera-IMU time offset modeled as an unknown variable that is able to handle the rolling-shutter effects and imperfect sensor synchronization in a unified way.
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