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ElasticFusion: real-time dense SLAM and light source estimation

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Title: ElasticFusion: real-time dense SLAM and light source estimation
Authors: Whelan, T
Salas-Moreno, RF
Glocker, B
Davison, AJ
Leutenegger, S
Item Type: Journal Article
Abstract: We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any post-processing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality (AR) rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric tracking
Issue Date: 29-Sep-2016
Date of Acceptance: 23-Aug-2016
URI: http://hdl.handle.net/10044/1/39502
DOI: https://dx.doi.org/10.1177/0278364916669237
ISSN: 1741-3176
Publisher: SAGE Publications (UK and US)
Start Page: 1697
End Page: 1716
Journal / Book Title: International Journal of Robotics Research
Volume: 35
Issue: 14
Copyright Statement: © The Author(s) 2016 Published by Sage Publications.
Sponsor/Funder: Dyson Technology Limited
Funder's Grant Number: PO 4500285622
Keywords: Industrial Engineering & Automation
0801 Artificial Intelligence And Image Processing
0906 Electrical And Electronic Engineering
0913 Mechanical Engineering
Publication Status: Published
Appears in Collections:Faculty of Engineering

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