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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Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency
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Persistent Stereo Visual Localization on Cross-Modal Invariant Map
TL;DR: A stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance is proposed.
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A Compatible Framework for RGB-D SLAM in Dynamic Scenes
TL;DR: This paper proposes a workflow to segment the objects accurately, which will be marked as the potentially dynamic-object area based on the semantic information, and integrates the semantics-based motion detection and the segmentation approach with an RGB-D SLAM system.
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Rethinking the sGLOH Descriptor
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TL;DR: The revised sGLOH descriptor incorporating the above ideas and combining them according to task requirements, improves in most cases the state of the art in both image matching and object recognition.
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