Double window optimisation for constant time visual SLAM

@article{Strasdat2011DoubleWO,
  title={Double window optimisation for constant time visual SLAM},
  author={Hauke Malte Strasdat and Andrew J. Davison and J. M. M. Montiel and Kurt Konolige},
  journal={2011 International Conference on Computer Vision},
  year={2011},
  pages={2352-2359}
}
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. [] Key Method Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects inner and outer window membership and optimises both simultaneously. We demonstrate in extensive simulation experiments that our method approaches the accuracy of offline bundle adjustment while maintaining…
Image based optimisation without global consistency for constant time monocular visual SLAM
  • Vincent Lui, T. Drummond
  • Computer Science
    2015 IEEE International Conference on Robotics and Automation (ICRA)
  • 2015
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References

SHOWING 1-10 OF 19 REFERENCES
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.
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
TLDR
The skeleton of this framework is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration.
Parallel Tracking and Mapping for Small AR Workspaces
TLDR
A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.
Highly scalable appearance-only SLAM - FAB-MAP 2.0
TLDR
A new formulation of appearance-only SLAM suitable for very large scale navigation that naturally incorporates robustness against perceptual aliasing is described and demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory.
Real Time Localization and 3D Reconstruction
TLDR
A method that estimates the motion of a calibrated camera and the tridimensional geometry of the environment and the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence is described.
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
TLDR
A new relative bundle adjustment is derived which, instead of optimizing in a single Euclidean space, works in a metric space defined by a manifold, and it is shown experimentally that it is possible to solve for the full maximum-likelihood solution incrementally in constant time, even at loop closure.
Online environment mapping
TLDR
The paper proposes a vision based online mapping of large-scale environments using a hybrid representation of a fully metric Euclidean environment map and a topological map that achieves scalability by solving the local registration through embedding neighboring keyframes and landmarks into a Euclidan space.
Adaptive relative bundle adjustment
TLDR
This paper derives a new relative bundle adjustment, which instead of optimizing in a single Euclidean space, works in a metric-space defined by a connected Riemannian manifold, and shows experimentally that it is possible to solve for the full ML solution incrementally in constant time – even at loop closure.
On measuring the accuracy of SLAM algorithms
TLDR
A framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory is proposed, which overcomes serious shortcomings of approaches using a global reference frame to compute the error.
G2o: A general framework for graph optimization
TLDR
G2o, an open-source C++ framework for optimizing graph-based nonlinear error functions, is presented and demonstrated that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.
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