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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.
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
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.
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.
The Replica Dataset: A Digital Replica of Indoor Spaces
TLDR
Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale, is introduced to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world.
Fast relocalisation and loop closing in keyframe-based SLAM
TLDR
A relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes, and permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera.
Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM
TLDR
This paper presents a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art.