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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.
Inverse Depth Parametrization for Monocular SLAM
We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed
Unified Inverse Depth Parametrization for Monocular SLAM
TLDR
This paper presents a new unified parametrization for point features within monocular SLAM which permits efficient and accurate representation of uncertainty during undelayed initialisation and beyond, all within the standard EKF (Extended Kalman Filter).
Scale Drift-Aware Large Scale Monocular SLAM
TLDR
This paper describes a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input and presents a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures.
Real-time monocular SLAM: Why filter?
TLDR
This paper performs the first rigorous analysis of the relative advantages of filtering and sparse optimisation for sequential monocular SLAM and concludes that while filtering may have a niche in systems with low processing resources, in most modern applications keyframe optimisation gives the most accuracy per unit of computing time.
Double window optimisation for constant time visual SLAM
We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level
1‐Point RANSAC for extended Kalman filtering: Application to real‐time structure from motion and visual odometry
Random sample consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from
Visual SLAM: Why filter?
Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines
This paper explores the impact that landmark parametrization has in the performance of monocular, EKF-based, 6-DOF simultaneous localization and mapping (SLAM) in the context of undelayed landmark
1-point RANSAC for EKF-based Structure from Motion
TLDR
Visual Odometry algorithms are nowadays able to estimate with impressive accuracy trajectories of hundreds of meters; either from an image sequence (usually stereo) as the only input, or combining visual and propioceptive information from inertial sensors or wheel odometry.
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