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Simultaneous super-resolution, tracking and mapping

Maxime Meilland 1, 2 Andrew I. Comport 1
2 AROBAS - Advanced Robotics and Autonomous Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper proposes a new visual SLAM technique that not only integrates 6DOF pose and dense structure but also simultaneously integrates the color information contained in the images over time. This involves developing an inverse model for creating a super-resolution map from many low resolution images. Contrary to classic super-resolution techniques, this is achieved here by taking into account full 3D translation and rotation within a dense localisation and mapping framework. This not only allows to take into account the full range of image deformations but also allows to propose a novel criteria for combining the low resolution images together based on the difference in resolution between different images in 6D space. Several results are given showing that this technique runs in real-time (30Hz) and is able to map large scale environments in high-resolution whilst simultaneously improving the accuracy and robustness of the tracking.
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Contributor : Andrew Comport <>
Submitted on : Friday, September 23, 2016 - 11:24:33 AM
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  • HAL Id : hal-01357366, version 1



Maxime Meilland, Andrew I. Comport. Simultaneous super-resolution, tracking and mapping. [Research Report] CNRS-I3S/UNS. 2012. ⟨hal-01357366⟩



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