scholarly journals Computing the sensory uncertainty field of a vision-based localization sensor

2002 ◽  
Author(s):  
A. Adam ◽  
E. Rivlin ◽  
I. Shimshoni
Keyword(s):  
Vision Based Localization ◽  
Sensory Uncertainty

scholarly journals Improved Visual Localization via Graph Filtering

2021 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
Carlos Lassance ◽  
Yasir Latif ◽  
Ravi Garg ◽  
Vincent Gripon ◽  
Ian Reid
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Visual Localization ◽  
Localization Accuracy ◽  
Additional Information ◽  
Feature Representations ◽  
The Mean ◽  
Classical Image ◽  
Vision Based Localization ◽  
Improve Reliability

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.

Communicating Vehicles in Convoy and Monocular Vision-based Localization

2010 ◽  
Vol 43 (16) ◽  
pp. 73-78 ◽  
Author(s):  
Thomas Féraud ◽  
Paul Checchin ◽  
Romuald Aufrère ◽  
Roland Chapuis
Keyword(s):  
Monocular Vision ◽  
Vision Based Localization

Quadrotor: Design, Control and Vision Based Localization

2014 ◽  
Vol 47 (1) ◽  
pp. 1104-1110 ◽  
Author(s):  
Anurag Sai Vempati ◽  
Vipul Choudhary ◽  
Laxmidhar Behera
Keyword(s):  
Vision Based Localization

scholarly journals Autonomous exploration with prediction of the quality of vision-based localization

2017 ◽  
Vol 50 (1) ◽  
pp. 10274-10279 ◽  
Author(s):  
Hélène Roggeman ◽  
Julien Marzat ◽  
Anthelme Bernard-Brunel ◽  
Guy Le Besnerais
Keyword(s):  
Autonomous Exploration ◽  
Quality Of Vision ◽  
Vision Based Localization

Vision-based Localization and Robot-centric Mapping in Riverine Environments

2015 ◽  
Vol 34 (3) ◽  
pp. 429-450 ◽  
Author(s):  
Junho Yang ◽  
Ashwin Dani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Vision Based Localization

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

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