Real-Time RGB-D Camera Relocalization via Randomized Ferns for Keyframe Encoding

Glocker, Ben; Shotton, Jamie; Criminisi, Antonio; Izadi, Shahram

doi:10.1109/tvcg.2014.2360403

Cited by 106 publications

(105 citation statements)

References 46 publications

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“…We utilise the randomised fern encoding approach of Glocker et al (2015) for appearance-based place recognition. Ferns encode an RGB-D image as a string of codes made up of the values of binary tests on each of the RGB-D channels in a set of fixed pixel locations.…”

Section: Global Loop Closurementioning

confidence: 99%

“…This technique has been demonstrated to perform very reliably in terms of computational performance and viewpoint recognition. Our implementation of randomised fern encoding is identical to that of Glocker et al (2015) with the difference that instead of encoding and matching against raw RGB-D frames, we use predicted views of the surface map once they are aligned and fused with the live camera view. Parts of the predicted views which are devoid of any mapped surface are filled in using the live depth and colour information from the current frame.…”

Section: Global Loop Closurementioning

confidence: 99%

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ElasticFusion: Real-time dense SLAM and light source estimation

Whelan

Salas-Moreno

Glocker

et al. 2016

The International Journal of Robotics Research

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612

461

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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.

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Section: Global Loop Closurementioning

confidence: 99%

Section: Global Loop Closurementioning

confidence: 99%

ElasticFusion: Real-time dense SLAM and light source estimation

Whelan

Salas-Moreno

Glocker

et al. 2016

The International Journal of Robotics Research

Self Cite

612

461

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“…The inactive portion of the map which caused this loop closure is then reactivated to allow tracking and surface fusion (including surfel culling) to take place between the registered areas of the map. 4) For global loop closure, add predicted views of the scene to a randomised fern encoding database [6]. Each frame, attempt to find a matching predicted view via this database.…”

Section: Approach Overviewmentioning

confidence: 99%

“…We utilise the randomised fern encoding approach for appearance-based place recognition [6]. Ferns encode an RGB-D image as a string of codes made up of the values of binary tests on each of the RGB-D channels in a set of fixed pixel locations.…”

Section: Global Loop Closurementioning

confidence: 99%

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Robotics: Science and Systems XI

2015

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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 explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any postprocessing 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.

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Addressing the challenges of loop detection in agricultural environments

Soncini,

Civera,

Pire

2024

Journal of Field Robotics

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While visual Simultaneous Localization and Mapping systems are well studied and achieve impressive results in indoor and urban settings, natural, outdoor, and open‐field environments are much less explored and still present relevant research challenges. Visual navigation and local mapping have shown a relatively good performance in open‐field environments. However, globally consistent mapping and long‐term localization still depend on the robustness of loop detection and closure, for which the literature is scarce. In this work, we propose a novel method to pave the way towards robust loop detection in open fields, particularly in agricultural settings, based on local feature search and stereo geometric refinement, with a final stage of relative pose estimation. Our method consistently achieves good loop detections, with a median error of 15 cm. We aim to characterize open fields as a novel environment for loop detection, understanding the limitations and problems that arise when dealing with them. Code is available at: https://github.com/CIFASIS/StereoLoopDetector

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Real-Time RGB-D Camera Relocalization via Randomized Ferns for Keyframe Encoding

Cited by 106 publications

References 46 publications

ElasticFusion: Real-time dense SLAM and light source estimation

ElasticFusion: Real-time dense SLAM and light source estimation

Robotics: Science and Systems XI

Addressing the challenges of loop detection in agricultural environments

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