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Open AccessProceedings

Dynamic Catadioptric Sensory Data Fusion for Visual Localization in Mobile Robotics

Department of Systems Engineering and Automation, Miguel Hernández University, Av. de la Universidad s/n. Ed. Innova., 03202 Elche (Alicante), Spain
Centre for Automation and Robotics (CAR), UPM-CSIC. Technical University of Madrid, C/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain
Author to whom correspondence should be addressed.
Presented at the 7th International Symposium on Sensor Science, Napoli, Italy, 9–11 May 2019.
Proceedings 2019, 15(1), 2;
Published: 5 July 2019
(This article belongs to the Proceedings of 7th International Symposium on Sensor Science)
This approach presents a localization technique within mobile robotics sustained by visual sensory data fusion. A regression inference framework is designed with the aid of informative data models of the system, together with support of probabilistic techniques such as Gaussian Processes. As a result, the visual data acquired with a catadioptric sensor is fused between poses of the robot in order to produce a probability distribution of visual information in the 3D global reference of the robot. In addition, a prediction technique based on filter gain is defined to improve the matching of visual information extracted from the probability distribution. This work reveals an enhanced matching technique for visual information in both, the image reference frame, and the 3D global reference. Real data results are presented to confirm the validity of the approach when working in a mobile robotic application for visual localization. Besides, a comparison against standard visual matching techniques is also presented. The suitability and robustness of the contributions are tested in the presented experiments.
Keywords: catadioptric sensor; visual data fusion; mobile robotics catadioptric sensor; visual data fusion; mobile robotics
MDPI and ACS Style

Valiente, D.; Payá, L.; Sebastián, J.M.; Jiménez, L.M.; Reinoso, O. Dynamic Catadioptric Sensory Data Fusion for Visual Localization in Mobile Robotics. Proceedings 2019, 15, 2.

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