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KinectFusion: Real-time dense surface mapping and tracking
We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.…
View on IEEE
MonoSLAM: Real-Time Single Camera SLAM
TLDR
The first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches is presented.
View on IEEE
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
TLDR
Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction, to enable real-time multi-touch interactions anywhere.
View on ACM
DTAM: Dense tracking and mapping in real-time
TLDR
It is demonstrated that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application is shown.
View on IEEE
Real-time simultaneous localisation and mapping with a single camera
  • A. Davison
  • Computer Science
    Proceedings Ninth IEEE International Conference…
  • 13 October 2003
TLDR
This work presents a top-down Bayesian framework for single-camera localisation via mapping of a sparse set of natural features using motion modelling and an information-guided active measurement strategy, in particular addressing the difficult issue of real-time feature initialisation via a factored sampling approach.
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A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
TLDR
This work introduces the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset and presents a collection of handheld RGB-D camera sequences within synthetically generated environments to provide a method to benchmark the surface reconstruction accuracy.
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KAZE Features
TLDR
KAZE features, a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces, can make blurring locally adaptive to the image data, reducing noise but retaining object boundaries, obtaining superior localization accuracy and distinctiviness.
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ElasticFusion: Dense SLAM Without A Pose Graph
TLDR
This 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.
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End-To-End Multi-Task Learning With Attention
TLDR
The proposed Multi-Task Attention Network (MTAN) consists of a single shared network containing a global feature pool, together with a soft-attention module for each task, which allows learning of task-specific feature-level attention.
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Inverse Depth Parametrization for Monocular SLAM
We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed…
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