Parallel Tracking and Mapping on a camera phone

Klein, Georg; Murray, David

doi:10.1109/ismar.2009.5336495

Cited by 437 publications

(280 citation statements)

References 12 publications

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“…Furthermore, there exist alternative processing models, including directly leveraging RGB-D data and foregoing the need for a mesh model. Surfaces for displaying content might be detected during geometry recovery using techniques such as Dense Planar SLAM [181] or Parallel Tracking and Mapping [113].…”

Section: Summary and Future Workmentioning

confidence: 99%

The personal cockpit

Ens

Finnegan

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Section: Summary and Future Workmentioning

confidence: 99%

The personal cockpit

Ens

Finnegan

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…We suggest a feature initialisation method for keyframe-based SLAM systems based on a set of three dimensional information filters which can estimate the position of arbitrarily distant features. Very recently, a similar method was briefly described by Klein and Murray [15]. Compared to our approach, their filters are applied on every frame, are one-dimensional and are only used for data association.…”

Section: Feature Initialisationmentioning

confidence: 99%

Scale Drift-Aware Large Scale Monocular SLAM

Strasdat¹,

Montiel²,

Davison³

2010

Robotics: Science and Systems VI

354

309

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Abstract-State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocular vision, since a single camera will always be cheaper, more compact and easier to calibrate than a multi-camera rig.With high quality estimation, a single camera moving through a static scene of course effectively provides its own stereo geometry via frames distributed over time. However, a classic issue with monocular visual SLAM is that due to the purely projective nature of a single camera, motion estimates and map structure can only be recovered up to scale. Without the known inter-camera distance of a stereo rig to serve as an anchor, the scale of locally constructed map portions and the corresponding motion estimates is therefore liable to drift over time.In this paper we describe a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input. In particular, we present a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures. Especially, we describe the Lie group of similarity transformations and its relation to the corresponding Lie algebra. We also present in detail the system's new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped trajectories.

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“…The proposed methods are essentially an extension of the idea of stereo reconstruction. The approaches for Simultaneous Localization And Mapping (SLAM) [2], [3], [4] usually provide an approximate reconstruction of the scene, achieving the result in nearly realtime. These methods rely on frame-to-frame video tracking, and they are not always reliable for a wide baseline case.…”

Section: Related Workmentioning

confidence: 99%

Assisted Multi-view Stereo Reconstruction

Dellepiane

Cavarretta

Cignoni

et al. 2013

2013 International Conference on 3D Vision

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Abstract-Multi-view stereo reconstruction methods can provide impressive results in a number of applications. Nevertheless, when trying to apply the state-of-the-art methods in the case of a more structured 3D acquisition, the lack of feedback on the quality of the reconstruction during the photo shooting can be problematic. In this paper we present a framework for the assisted reconstruction from images of real objects. The framework is able to provide, in quasi-realtime, a sparse reconstruction of the scene, so that the user is able to spot the missing or problematic parts. Moreover, the framework is able to separate the object of interest from the background and suggests missing points of view to the user, without any previous knowledge of the shape of the scene and the acquisition path. This is obtained by analyzing the sparse reconstruction and the connection between the reconstructed points and the input images. The framework has been tested on a variety of practical cases, and it has proved to be effective not only to obtain more complete reconstructions, but also to reduce the number of images needed and the processing time for dense reconstruction.

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Parallel Tracking and Mapping on a camera phone

Cited by 437 publications

References 12 publications

The personal cockpit

The personal cockpit

Scale Drift-Aware Large Scale Monocular SLAM

Assisted Multi-view Stereo Reconstruction

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