Fast relocalisation and loop closing in keyframe-based SLAM

@article{MurArtal2014FastRA,
  title={Fast relocalisation and loop closing in keyframe-based SLAM},
  author={Raul Mur-Artal and Juan D. Tard{\'o}s},
  journal={2014 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2014},
  pages={846-853}
}
  • Raul Mur-Artal, J. D. Tardós
  • Published 29 September 2014
  • Computer Science
  • 2014 IEEE International Conference on Robotics and Automation (ICRA)
In this paper we present for the first time a relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes. As this method relies on local features, it permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera. We also perform loop closing (detection + correction), at keyframerate, in loops containing hundreds of keyframes. For both relocalisation and… 
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193 Citations
Highly Influential Citations
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Methods Citations
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193 Citations

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Citation Type

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TLDR
An active loop closure based relocalization system, which enables the monocular SLAM to detect and recover from tracking failures automatically even in previously unvisited areas where no keyframe exists, and is more robust than others.
Keyframe-Based Camera Relocalization Method Using Landmark and Keypoint Matching
TLDR
This paper presents an accurate method for RGB camera relocalization in the case of a very sparse 3D map built by limited keyframes by using the landmark-based place recognition method to generate from keyframes the images similar to the current view along with the set of pairwise matched landmarks.
RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping
TLDR
A complete SLAM system for RGB-D cameras, namely RGB-iD SLAM, which is a dense direct SLAM method with the main characteristic of working with the depth maps in inverse depth parametrisation for the routines of dense alignment or keyframe fusion.
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.
Using collaborative sharing on cloud for fast relocalization in keyframe-based SLAM
TLDR
This paper presents an approach based on cloud-based sharing, which aims at providing a way for fast relocalization on the existing keyframe-based SLAM framework, and proposes various mechanisms to eliminate the degeneration of this distributed model and the unstable network.
RELOCALIZATION AND LOOP CLOSING IN VISION SIMULTANEOUS LOCALIZATION AND MAPPING (VSLAM) OF A MOBILE ROBOT USING ORB METHOD
TLDR
The accuracy of relocalization and loop closing process using ORB SLAM algorithm in a variety of environmental settings is shown and the applicability of the approach in real time application like autonomous navigation is shown.
LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection
TLDR
This paper introduces LiPo-LCD, a novel appearance-based loop closure detection method that integrates lines and points that compares well with several state-of-the-art solutions for a number of datasets involving different environmental conditions.
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
TLDR
This paper shows how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly, and achieves relocalisation performance that is on par with that of offline forests, and the approach runs in under 150ms, making it desirable for real-time systems that require online Relocalisation.
Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM
TLDR
This work proposes a new Robust Multimodal Sequence-based (ROMS) method for robust loop closure detection in long-term visual SLAM, and develops a new algorithm to solve the formulated optimization problem efficiently, which guarantees to converge to the global optima theoretically.
FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
TLDR
An RGB-D SLAM system that leverages the advantages of both approaches: using dense frame-to-model odometry to build accurate sub- maps and on-the-fly feature-based matching across sub-maps for global map optimisation.
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