Journal ArticleDOI
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Mark Cummins,Paul Newman +1 more
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TLDR
A probabilistic approach to the problem of recognizing places based on their appearance that can determine that a new observation comes from a previously unseen place, and so augment its map, and is particularly suitable for online loop closure detection in mobile robotics.Abstract:
This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics.read more
Citations
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Journal ArticleDOI
Variational Inference: A Review for Statisticians
TL;DR: For instance, mean-field variational inference as discussed by the authors approximates probability densities through optimization, which is used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling.
Journal ArticleDOI
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cesar Cadena,Luca Carlone,Henry Carrillo,Yasir Latif,Davide Scaramuzza,José Neira,Ian Reid,John J. Leonard +7 more
TL;DR: Simultaneous localization and mapping (SLAM) as mentioned in this paper consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it.
Journal ArticleDOI
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Cesar Cadena,Luca Carlone,Henry Carrillo,Yasir Latif,Davide Scaramuzza,José L. Neira,Ian Reid,John J. Leonard +7 more
TL;DR: What is now the de-facto standard formulation for SLAM is presented, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers.
Proceedings ArticleDOI
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
TL;DR: A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task and an efficient training procedure which can be applied on very large-scale weakly labelled tasks are developed.
Proceedings ArticleDOI
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
TL;DR: PoseNet as mentioned in this paper uses a CNN to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation.
References
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